GC21A-0126
NARCCAP - First Analyzes Concerning the Snow Regime of the Upper Colorado River Basin
One of the main goals of Regional Climate Models (RCMs) is to provide high resolution climate (scenario) data for further use by the impact community. RCMs have been proven to be especially valuable over regions with heterogeneous surface such as mountain ranges. With the North American Regional Climate Change Program (NARCCAP), there is now a promising source of RCM runs (for current and scenario climate conditions) to serve the climate impacts community of North America. First NARCCAP runs have been made available very recently. In this presentation, we show and discuss first analyzes of NCEP-driven NARCCAP runs. Thereby, we center on the RCM performance regarding the seasonal snow regime of the Rocky Mountains, with special focus on the region of the Upper Colorado River Basin (UCRB). The streamflow of the seasonal snow cover of the UCRB is the major water resource for the Colorado River and thus also for millions of people living in the surrounding areas. In a further step of this study, it is therefore planned to use results from NARCCAP model runs as input variables to run a hydrological runoff model. This will allow us to assess the impact of changes in the snow regime on the availability of freshwater for the regions depending on the water supplied by the Colorado River.
GC21A-0127
Lessons Learned From NARCCAP on Archiving Data and Meeting User Needs
The North American Regional Climate Change Assessment Program (NARCCAP) is an international program to produce high resolution climate change scenarios and investigate uncertainties in regional scale projections of future climate by nesting multiple regional climate models (RCMs) within multiple atmosphere-ocean general circulation models (AOGCMs) forced with the A1B SRES scenario over a domain covering the conterminous United States and most of Canada. The resulting datasets will total more than 40 terabytes in size and must be archived for distributed storage and made available to global change impacts researchers worldwide via the Earth System Grid (ESG). This presentation will describe the steps necessary to accomplish these geoscience data management goals and lessons we have learned along the way about handling such an enormous flux of data, maintaining its quality and integrity, and ensuring that the final product is usable by the impacts community, GIS practitioners, and other end users. The importance of data formats, metadata standards, and flexible tools for visualization, checking, and automation will be discussed, as well as social and other significant factors. http://www.narccap.ucar.edu/
GC21A-0128
Trends in US Surface Winds Over the Last Quarter of the 20C: Observations and Model Results
Trends in near-surface wind speed are of interest for wind power production, land-atmosphere interactions, ocean-atmosphere interactions, agricultural applications and many other purposes. Changes in wind speed can create changes in surface fluxes of heat, momentum, moisture, and trace gases. These, in turn, can affect low- level atmospheric stability and boundary-layer depth, thereby feeding back to the ability of the boundary layer to extract momentum from the free atmosphere. Impact of climate change on surface wind speeds has received relatively little attention despite the important impact of wind speed changes on fundamental balances of heat, momentum, and water at the earth's surface. We have examined trends in near-surface wind speeds from observations and results of a regional climate model. Pryor et al. (2007) reported reductions in wind speed generally in the eastern half of the US in the latter quarter of the twentieth century. To examine whether regional climate models can capture such trends we have examined output of such a model (MM5) for the US for the period 1979-2004 produced under the North American Regional Climate Change Assessment Program. This model was driven by reanalysis boundary conditions updated at 6-h intervals at the lateral boundaries. Preliminary results indicate that the model also reveals a general decline in wind speeds in the eastern US and a few isolated regions of wind speed decline in the western US. At almost no grid points over the continental US did the model produce wind speed increases over the latter quarter of the twentieth century. More detailed analyses of trends in seasonal and diurnal distributions are in progress.
GC21A-0129
Downscaling ENSO Events in Nested Regional Climate Models
We have examined the ability of an ensemble of nested regional climate models (RCMs) to provide regional detail from large-scale information during El Nino-Southern Oscillation events. This research is a component of the North American Regional Climate Change Assessment Program (NARCCAP), which is evaluating propagation of uncertainty when nested regional climate models (RCMs) are used to dynamically downscale climate projections from atmosphere-ocean general circulation models (AOGCMs). In the present application, RCM uncertainty is isolated by nesting the six NARCCAP RCMs within reanalyses of observations (the NCEP- DOE Reanalysis-2) for the period 1979-2004. The period contains two strong El Nino events (1982-83 and 1997- 98) and several other El Nino and La Nina episodes. Results show that the ENSO signal in precipitation is well represented for the west coast of North America in all of the RCMs. In that region the timing of both onset and withdrawal of ENSO-induced precipitation anomalies corresponded closely with observations, as did the spatial distribution of precipitation. ENSO-induced precipitation anomalies in the southern and eastern United States were depicted less accurately and varied more from model to model. Taking these results together with other recent findings which show that some AOGCMs can produce realistic representations of the El Nino-Southern Oscillation (ENSO), we propose that a coupled AOGCM-RCM system may provide useful projections of ENSO and its regional effects in future climates. http://www.narccap.ucar.edu/
GC21A-0130
Comparison of Regional Climate Simulations Driven by a Global Reanalysis and a Global Climate Simulation Over North America
As part of the North American Regional Climate Change Assessment Program (NARCCAP), two regional climate simulations were performed for 1979 - 2005 for North America based on the Penn State/NCAR MM5 and Weather Research and Forecasting model (WRF) driven by the NCEP/DOE global reanalysis and AMIP sea surface temperature. Although MM5 and WRF share many common physics parameterizations, there are apparent differences in how well the models simulate precipitation and the surface energy and water budgets and their interannual variations. Analyses are being performed to eluciate the causes for model differences in both warm and cold season regimes. Additionally, WRF is being used to downscale global climate simulations for the present and future conditions. By comparing regional simulations (MM5 and WRF) driven by the same global reanalysis and regional simulations (WRF) driven by the global reanalysis and global climate simulation, we will examine the relative control of the large scale boundary conditions and physics parameterizations on the regional simulations.
GC21A-0131
A case study of mineral dusts from China and Mongolia: satellite remote sensing, meteorological analysis and model simulation
We used MODIS L1B data to monitor the dust events during April 6—11, 2001; then employed the NCEP/NCAR reanalysis Project daily dataset to analyze the physical mechanism of dust emission, transport and deposition process. And finally, we introduced RegCM into Asian dust simulation. The study area is northern China and southern Mongolia (between 70–150°E and 20–60°N), where are regarded as the main dust sources in Asia. During April 6—11, huge dust storms developed from the south of Mongolia and the north of China, sweeping across northern China, southeastern Russia, Korean Peninsula, Japan, North Pacific Ocean. In this period, the dust events consisted of 2 episodes. One was mainly from southeast of Mongolia (Dundgovi and Dornogovi) and the middle of Inner Mongolia (Otintag sandy land), China. The other was mainly from Taklimakan Desert. In the first episode, the dust was raised from the southeast of Mongolia on April 6, and moved eastward. On April 7, the dust blow across Otintag sand land of Inner Mongolia, China; the loose, plentiful dust materials here and the funneling cased by the mountain ranges of Xinganling to the east strengthened the dust; the dust storm almost covered the whole northeast of China. In the following days, the dust storm moved northeastward slowly, went across southeast of Russia and then to the North Pacific Ocean. This episode was associated with Mongolian cyclone. On April 5 (00:00), a trough was to the east of Lake Baikal. Under the influence of cold air from north, a Mongolian cyclone formed here around 12:00 April 5, with a center of 995 geopotential height (gpm). This cyclone deepened, while it moved quickly eastward. When this cyclone swept across the south of Mongolia and the middle of Inner Mongolia, strong dust storm occurred. Although another cyclone formed to the southwest of Lake Baikal around 06:00, April 7, the first cyclone was relatively stronger. The uprising air of this cyclone can also be well explained by positive vorticity at 500 hpa. The positive vorticity area lay in the east of Mongolia and the east of Inner Mongolia, China, where is just between on the transport pathway of the dust storm originated from south of Mongolia and middle of Inner Mongolia, as shown by satellite images of April 6 and April 7. The strong wind, surface convergence and upper divergence derived from NNRP1 data also explained this episode well. The second episode mainly originated from Takalimakan Desert. On April 8, strong local wind developed in the basin; and carried dust out of the basin, across Mongolia and northern China on April 9, to the northeastern China on April 10 and the North Pacific Ocean on April 11. The circulation of strong local wind brought dust from the edges of the desert out of the basin through the east exit on the border of Xinjiang, Gansu of China, where lies between Tianshan Mountains (average elevation >3500 meters) and Mazongshan Mountains (average elevation between 1000 and 3500 meters). This episode demonstrated a northeastward transport of dust from Taklimakan Desert, as differs from previous reports. This episode was caused cold front, associated upper trough-ridge system. The NNRP1 derived negative divergence over the northeast rim of Taklimakan Desert well explained the dust sources. RegCM was used to simulate these dust events. The results showed that dust module may overestimate the dust emission from some deserts, especially Gurban Tonggut Desert, and underestimate dust emission from Gobi desert and sandy land, which have been identified as the main contributor to Asian dust.
GC21A-0132
South Asian Summer Monsoon Dynamics In A High-Resolution Nested Climate Model
We present results from a high-resolution climate simulation of the south Asian monsoon using the Abdus Salam Centre for Theoretical Physics Regional Climate Model (RegCM3). The RegCM3 experiment consists of a 30-year integration from 1961 to 1990 performed at a 25 km grid spacing. Atmospheric boundary conditions for the integration are provided by the National Aeronautics and Space Administration (NASA) Finite Volume General Circulation Model (FVGCM). The ability of RegCM3 to simulate the dynamics of the summer monsoon is tested by comparing a number of fields with observations, including upper and lower level circulation patterns, seasonal mean precipitation and temperature, and variations in tropospheric temperature gradient and easterly vertical shear. Our results show that RegCM3 is able to simulate the dynamical features of the South Asian summer monsoon reasonably well. For instance, the seasonal reversal of tropospheric temperature gradient and strengthening of easterly vertical shear compare well with observations. Furthermore, summer monsoon onset dates over land match reasonably well with the long-term onset-climatology, and the interannual variations in the anomalies of the local Hadley circulation and summer monsoon precipitation are strongly correlated. The primary discrepancies occur over areas of high seasonal precipitation – such as the west coasts of India and Myanmar – where RegCM3 values exceed those found in the observations. Similarly, RegCM3 overestimates precipitation values on the lee side of the Western Ghats. Compared to the driving FVGCM simulation, the RegCM3 simulation shows significant improvement in spatial pattern of seasonal precipitation.
GC21A-0133
Mesoscale Computer Modeling of the North American Monsoon in Arizona
An important question for the predictability of the North American Monsoon and related weather patterns is: what are the factors determining a favorable circulation and moisture configuration for the monsoon development. We utilize mesoscale models, radar, and satellite remote sensing data to explore the role of the SST, surge events in the Gulf of Baja-California, and Arizona topography in the evolution of the monsoonal convection propagation. Using advanced mesoscale models such as the PSU-NCAR fifth generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) mesocale numerical weather prediction system helps us study the variations of the summer precipitation regime in Northern Arizona and over the monsoon domain. This study seeks to improve our physical understanding of the North American monsoon (NAM) system and its evolution over Arizona. Our work targets: (1) better understanding and more realistic simulation of the evolution of the North American monsoon system and its variations in Northern Arizona, over a complex terrain; (2) better understanding and more realistic simulation of the response of the warm season atmospheric circulation and precipitation patterns to slowly varying boundary conditions (e.g. sea surface temperatures - SST, terrain, vegetation, soil moisture), using regional computer models. The study focuses on model simulations, analysis of observed and simulated data, and the development of a simple conceptual model; We compare model forecasts for the onset of Arizona monsoon rainfall against radar, and satellite observations, and discuss the precipitation implications for the monsoon season in the complex terrain of Northern Arizona as well as related impacts over the monsoon domain.
GC21A-0134
Urban Heat Islands and Their Mitigation vs. Local Impacts of Climate Change
Urban heat islands and their mitigation take on added significance, both negative and positive, when viewed form a climate-change perspective. In negative terms, urban heat islands can act as local exacerbating factors, or magnifying lenses, to the effects of regional and large-scale climate perturbations and change. They can locally impact meteorology, energy/electricity generation and use, thermal environment (comfort and heat waves), emissions of air pollutants, photochemistry, and air quality. In positive terms, on the other hand, mitigation of urban heat islands (via urban surface modifications and control of man-made heat, for example) can potentially have a beneficial effect of mitigating the local negative impacts of climate change. In addition, mitigation of urban heat islands can, in itself, contribute to preventing regional and global climate change, even if modestly, by helping reduce CO2 emissions from power plants and other sources as a result of decreased energy use for cooling (both direct and indirect) and reducing the rates of meteorology-dependent emissions of air pollutants. This presentation will highlight aspects and characteristics of heat islands, their mitigation, their modeling and quantification techniques, and recent advances in meso-urban modeling of California (funded by the California Energy Commission). In particular, the presentation will focus on results from quantitative, modeling-based analyses of the potential benefits of heat island mitigation in 1) reducing point- and area-source emissions of CO2, NOx, and VOC as a result of reduced cooling energy demand and ambient/surface temperatures, 2) reducing evaporative and fugitive hydrocarbon emissions as a result of lowered temperatures, 3) reducing biogenic hydrocarbon emissions from existing vegetative cover, 4) slowing the rates of tropospheric/ground-level ozone formation and/or accumulation in the urban boundary layer, and 5) helping improve air quality. Quantitative estimates of the above will be presented based on recent and earlier meteorological, energy, thermal environmental, emissions, and photochemical modeling studies for California and Texas.
GC21A-0135
Regional Modeling Applications of a Global Climate Model Using Local Mesh Refinement
Traditional regional climate models (RCMs) are limited area models that require lateral boundary conditions to be specified from an outside source, such as a global climate model (GCM). Information flow at the lateral boundaries is inward only. Events simulated within the RCM cannot propagate outside to the GCM domain, and thus cannot impact global flow patterns which in turn feed back into the RCM through the lateral boundaries. This restriction limits the applicability of scientific problems that can be investigated, for example, assessing the impact of local changes in climate forcing on the local climate. The Ocean-Land-Atmosphere Model (OLAM) is a new numerical simulation model that is based on the RAMS regional model but encompasses a global domain and uses a geodesic triangular mesh. Local mesh refinement enables OLAM to function simultaneously as a GCM and RCM. Selected geographic areas are represented with very high resolution typical of RCMs, while the remainder of the globe is covered with a lower resolution GCM-like grid. This configuration enables two-way information transfer between low and high resolution regions, eliminates problematical lateral boundary conditions, and allows simulations of comprehensive cause and effect relationships between proximate or widely separated events within a single model system. It is common for the refined mesh regions to contain more than half the grid cells in the model domain and to require most of the computational steps. Therefore, compared with an RCM, the OLAM coarse global grid adds a modest increase in computational expense while providing major benefits. Different mesh structures that provide local refinement are compared. Standard global simulation tests are used to evaluate performance of different refinement structures and as a guide for optimizing them, particularly in the transitional region between lower and higher resolution. Techniques are discussed to enable physical parameterizations to adapt to a wide range of grid resolution. Simulations of shallow and deep convective systems in highly refined regions of the grid test model performance at the finest scales.
GC21A-0136
Dynamical Downscaling Technique for Global Climate Model
Aiming at producing higher resolution global reanalysis datasets from coarse 200 km resolution reanalysis, a global version of the dynamical downscaling using a global spectral model (GSM) is developed. A variant of spectral nudging, the scale-selective bias correction (SSBC) developed for regional models is modified in the following manner to adapt it to the global domain; 1) temperature is nudged in addition to the zonal and meridional components of winds, and 2) humidity is excluded from any nudging or correction. The downscaling was performed using T248L28 (about 50 km resolution) global model for 2001, driven by NCEP/NCAR Reanalysis 2 (T62L28 resolution). Evaluation with high-resolution observations showed that the monthly averaged surface temperature and daily variation of precipitation become better than the Reanalysis over the globe. It was found that humidity plays a significant role for a significant positive bias of global precipitation in the downscaled simulation. Over North America, surface wind speed and temperature become better, and over Japan, the diurnal pattern of surface temperature is much improved, as are wind speed and precipitation, but not humidity. This study suggests that the global downscaling is a viable and economical method to obtain high- resolution reanalysis without re-running a very expensive high-resolution full data assimilation.
GC21A-0137
The JIFRESSE Regional Earth System Model and its application to air quality in California
In order for comprehensive assessments of the impact of global climate change on our society and ecosystems, the UCLA Joint Institute for Regional Earth System Science and Engineering (JIFRESSE) is developing a regional earth system model (RESM). The joint UCLA-JPL effort includes the development of a coupled regional atmosphere-land-ocean model. The core of the RESM is based on the Weather Research Model (WRF) for atmospheric processes, Simplified Simple Biosphere (SSiB) model for land-surface processes and the Regional Ocean Model (ROM) for coastal ocean processes. A suite of models for air pollution, hydrology, water resources, and ecosystem are to be added to the RESM in order to comprehensively assess the impact of climate change on regional sectors. This approach for the development of the JIFRESSE RESM attempts to balance physically- based simulations of important interactive processes within the climate system and the flexibility in the application of the RESM results to a variety of impact assessment studies. In line with the preceding objective, an air pollution model, the Community Multi-scale Air Quality (CMAQ) model, is currently used to assess the impact of regional climate elements on regional air quality – an aspect that is among the most important concerns in the assessment of the impact of future climate change on human and natural sectors. We will present preliminary results from the air quality assessment study in which the impact of anthropogenic emissions on air pollutants in Southern California will be investigated. Using the fine-resolution atmospheric forcing from the JIFRESSE RESM, the CMAQ model will be run with two sets of emission data, one for natural emission sources and the other for both natural and anthropogenic sources. Results from the two simulations will be differentiated to obtain the impact of anthropogenic emissions on air pollutants in California.
GC21A-0138
An Assessment of Two Statistical Downscaling Techniques for Generating Daily Climate Data for Central Canada
General Circulation Models, or Global Climate Models (GCMs), are widely used to assess potential impacts of global climate change because they are designed to simulate the present climate and project future climate. They however are not designed for local climate change impact studies and do not permit a good estimation of hydrological responses to climate change by themselves because of their coarse spatial scales. Statistical downscaling techniques have recently emerged as useful tools to convert the GCM outputs into a scale useful for climate change impact studies. These techniques are able to generate scenarios for a local site by using a statistically based model to represent the relationship between large scale climate variables and local climate variables. To date there have been several statistical downscaling techniques proposed in the scientific literature, each having its own advantages and shortcomings. Since there are many factors which can influence a downscaling model, such as the topography of the region, it is essential that a rigorous evaluation of the different statistical downscaling methods be undertaken. This will ensure that the most suitable approach is chosen to meet the conditions of the region. The objective of this study is to test two popular statistical downscaling methods, the Statistical Downscaling Model (SDSM) and the Stochastic Weather Generator (LARS-WG) for their ability to simulate daily time series of local precipitation and temperature for meteorological stations located in Central Canada. The evaluation will not only consist of examining the models ability to simulate means but will also examine their ability to simulate the magnitude and occurrence of extremes. These models will then applied to the GCM output from the Canadian Center for Climate Modeling and Analysis (CCCma) third generation model, CGCM3 T47 using the SRESA1, SRESA1B, and SRESA2 scenarios for two future time periods (2046-2065, 2081-2091) to project future climate change scenarios for these sites.
GC21A-0139
Regional Climate Modeling over the Marmara Region, Turkey, with Improved Land Cover Data
Land surface controls the partitioning of available energy at the surface between sensible and latent heat,and controls partitioning of available water between evaporation and runoff. Current land cover data available within the regional climate models such as Regional Atmospheric Modeling System (RAMS), the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) was obtained from 1- km Advanced Very High Resolution Radiometer satellite images spanning April 1992 through March 1993 with an unsupervised classification technique. These data are not up-to-date and are not accurate for all regions and some land cover types such as urban areas. Here we introduce new, up-to-date and accurate land cover data for the Marmara Region, Turkey derived from Landsat Enhanced Thematic Mapper images into the WRF regional climate model. We used several image processing techniques to create accurate land cover data from Landsat images obtained between 2001 and 2005. First, all images were atmospherically and radiometrically corrected to minimize contamination effects of atmospheric particles and systematic errors. Then, geometric correction was performed for each image to eliminate geometric distortions and define images in a common coordinate system. Finally, unsupervised and supervised classification techniques were utilized to form the most accurate land cover data yet for the study area. Accuracy assessments of the classifications were performed using error matrix and kappa statistics to find the best classification results. Maximum likelihood classification method gave the most accurate results over the study area. We compared the new land cover data with the default WRF land cover data. WRF land cover data cannot represent urban areas in the cities of Istanbul, Izmit, and Bursa. As an example, both original satellite images and new land cover data showed the expansion of urban areas into the Istanbul metropolitan area, but in the WRF land cover data only a limited area along the Bosporus is shown as urban. In addition, the new land cover data indicate that the northern part of Istanbul is covered by evergreen and deciduous forest (verified by ground truth data), but the WRF data indicate that most of this region is croplands. In the northern part of the Marmara Region, there is bare ground as a result of open mining activities and this class can be identified in our land cover data, whereas the WRF data indicated this region as woodland. We then used this new data set to conduct WRF simulations for one main and two nested domains, where the inner-most domain represents the Marmara Region with 3 km horizontal resolution. The vertical domain of both main and nested domains extends over 28 vertical levels. Initial and boundary conditions were obtained from National Centers for Environmental Prediction-Department of Energy Reanalysis II and the Noah model was selected as the land surface model. Two model simulations were conducted; one with available land cover data and one with the newly created land cover data. Using detailed meteorological station data within the study area, we find that the simulation with the new land cover data set produces better temperature and precipitation simulations for the region, showing the value of accurate land cover data and that changing land cover data can be an important influence on local climate change.
GC21A-0140
Impacts of improved land surface representations in a regional climate model with use of MODIS land surface temperature
As much attention has been given to quantify the climatic impacts of human modification of landscape at regional to global scales, realistic parameterization of key biophysical variables in climate models becomes imperative. Remotely sensed biophysical data have recently been employed to improve the land surface representation in climate modeling. Vegetation fractional cover (VFC) is such an important variable which decides the proportions of soil and vegetation in an area. In this paper, newly developed VFC data from the Moderate Resolution Imaging Spectroradiameter (MODIS) are incorporated in the Regional Atmospheric Modeling System (RAMS), replacing the built-in VFC specified by unrealistic mathematical equations. RAMS was run 12 months in 2003 with study area focused on East Africa region, which has one of the most complex landscape in the world. One big issue of climate simulation in this region is its lack of spatially and temporally explicit station observations for model validation. Land surface temperature (LST) products from MODIS Terra and Aqua were used to compare with the RAMS simulated LST. This study found that the built-in VFC in RAMS is too homogeneous spatially to differentiate distinct land surface types across the domain. Temporally, the prescribed VFC varies little with season especially for low-latitude regions such as East Africa. By using MODIS VFC, the simulated LST was greatly improved. Specifically, the bimodal feature of the LST seasonal variation, which is completely missed in the default land surface configurations, is better captured when MODIS VFC is incorporated. Regarding to the spatial characteristics, the Intertropical Convergence Zone (ITCZ) related seasonal migration of LST in the eastern domain has been greatly enhanced. Both MODIS Terra and Aqua LST are used for the first time to evaluate the surface impact on diurnal LST characteristics. This study found that the diurnal LST cycles in the second half of the year are slightly improved due to the new land surface representation. However, the improved land surface has little influence on simulated precipitation over 12 month period.
GC21A-0141
Preliminary Results of High Resolution Regional Climate Simulations in EC FP6 Project CECILIA: Impact of High Resolution on Reproducing Extremes
Project EC FP6 CECILIA – Central and Eastern Europe Climate Change Impact and Vulnerability Assessment is studying the impact of climate change in complex topography of the Central and Eastern Europe in high resolution. The impacts on agriculture, forestry, hydrology and air-quality are studied. Resolution of regional climate simulation is an important factor affecting the accuracy of dynamical downscaling of the global changes. Especially the extremes are strongly dependent on the terrain patterns as shape of orography or land use, which can contribute to extreme temperatures or precipitation appearance. Here the preliminary results of ERA40 reanalysis run at 10 km will be compared to previous results at 45 km from the experiment launched in connection to 2002 floods in Czech Republic, where we started to analyze whether RCMs are capable to reproduce extremes that can be quite important feature of changing climate. The experiments are compared in terms of mean temperature and extremes, other characteristics as the days with characteristic temperatures and heatwaves are analized as well. Some precipitation characteristics are compared, too. In the comparison to the real station data for Czech Republic it can be seen there is quite good agreement for 10 km simulation in temperature characteristics, there are still some problems with overestimation of small precipitation and underestimation of high precipitation by the model. The test of double nesting vs. direct forcing by reanalysis will be presented, on the selcted domain of quite big size the benefit of the double nesting can be seen against the results with direct driving of the model by ERA40 data.
GC21A-0142
Downscaling Hydroclimate Change Over Western US Based on CAM Subgrid Scheme and WRF Regional Climate Simulations
Global and regional climate simulations have been performed to compare two dynamical downscaling methods for simulating orographic effects and projecting the hydrologic impacts of climate change in the western U.S. The first approach applies a subgrid parameterization in a global climate model (the Community Atmosphere Model: CAM3) to simulate orographic effects. The second approach uses a regional climate model (Weather Research and Forecasting: WRF) to explicitly resolve the effects of orography on clouds and precipitation. Two 10-year simulations were completed for the present (1993-2003) and future (2039-2049) with CAM3 applied at 1x1.25 degree spatial resolution with the subgrid orographic precipitation scheme. Downscaling was performed using WRF driven by the CAM3 simulation for the two 10-year periods at 15 km spatial resolution for the western US. Precipitation, temperature, runoff, and snowpack simulated by CAM3 and WRF for 1993-2003 were evaluated using observations. We also compared the surface water budgets changes as well as extreme precipitation and runoff changes between 2039-2049 and 1993-2003. The large scale spatial distributions of precipitation changes are generally consistent between the WRF and CAM3 simulations. However, the WRF simulation indicates larger changes of precipitation along the coastal mountains (Cascades in the Northwest and Sierra Nevada in California) than CAM3. As the WRF model explicitly simulates the interactions of regional atmospheric circulation and the underlying topography, changes in winds in the future climate can lead to larger changes in orographic precipitation than that caused by changes in atmospheric moisture and temperature alone. The 95th percentile precipitation change is 3-5 times larger than the mean precipitation change in the winter. With the annual mean temperature increase of 0.5-2°C over western US, snowpack is significantly reduced by 40-60% over the mountain areas in the Pacific Northwest. Driven by the combined effects of precipitation change and snowmelt change due to warmer temperature, the runoff change shows a complicated spatial and seasonal variability. Our simulation experiments suggest significant impacts of greenhouse warming on mountain precipitation, snowpack and runoff that influence water resources. This study has also identified weaknesses in both downscaling methods and directions for future improvements.
GC21A-0143
Evaluation of Orographic Effect on Surface Climate with WRF Climate Model
Over high altitude regions, surface observations are scarce and representations of atmospheric and terrestrial flux exchanges can be poor in global models. This study evaluates the statistical dependence of various surface climate properties, such as temperature, moisture and winds on orographic elevation using the Weather and Research Forecast model (WRF). Our study region is the Tibetan Plateau where the elevation is greater than 3000m. Both seasonal and diurnal elevation dependences are analyzed and compared with global model simulations. The lapse rate of surface temperature is found to be smaller in the summer and larger in the winter, while opposite seasonality is seen for water vapor, likely due to the high surface moisture availability in the summer. The surface winds increase with altitude and has relatively larger gradients in all seasons except the summer. As for the diurnal cycle, the temperature decreases largely with elevation in the afternoon, probably due to the heating at the surface during daytime compared to the relatively stable conditions during the night and morning time within the planetary boundary layer. Water vapor shows a similar cycle as temperature; however, horizontal winds do not have a significant cycle. By testing the different cumulus and planetary boundary parameterizations available in the WRF model, it is found only small differences in their derived dependences, e.g., the orographic effect on surface temperature varies at most by 15 percent. The orographic effects from the regional simulations are generally in line with that of a global model.
GC21A-0144
A Regional Ocean Climate Model for the Mediterranean Sea With a Two-way Grid Refinement at the Strait of Gibraltar
The Mediterranean Sea is connected to the Atlantic ocean through the Strait of Gibraltar, a narrow and shallow channel, 60 km long and 20 km wide, characterized by a complex system of contractions and sills. The mean circulation within the strait of Gibraltar is an inverse estuarine circulation, characterized by a two-way exchange, with an upper flow of fresh and warm Atlantic water spreading in the Mediterranean basin, and a lower flow of cold and salty Mediterranean water sinking in the North Atlantic down to a depth of around 1000 m where it becomes neutrally buoyant. While this mean circulation is driven by an excess of evaporation over precipitation and river runoff in the Mediterranean sea, its magnitude and hydrological properties strongly depend on the physical configuration of the strait. In fact, it is well known that the water exchange within the strait, is subject to hydraulic control. The actual Mediterranean Sea regional models are typically characterized by an horizontal resolution ranging from 1/8o up to 1/16o. These resolutions are not able to describe the complex bathymetry of the strait, and so they are not able to reproduce the correct two-way exchange in terms of heat, salt and volume fluxes. To overcome this issue we apply the two-way grid refinement technique to the strait region; in particular the whole Mediterranean basin has been implemented using the MITgcm at 1/8o×1/8o (the ocean model component of the Protheus system), while the same model in the strait region reaches a finer resolution of 1/24o. Here we stress that 1/24o is the minimum resolution needed to represent in a reasonable way all the bathymetric features of the strait. In order to evidence the influence of the strait dynamics on the internal Mediterranean thermohaline circulation, two experiments, reproducing the actual climate conditions, were performed with and without grid-refinement. Results from these two experiments will be presented in detail and the major differences will be discussed.
GC21A-0145
A high-resolution climate model for the United States Pacific Northwest
Simulations of future climate scenarios with a high-resolution climate model show markedly different trends in temperature and precipitation over the Pacific Northwest than a global model in which it is nested, apparently due to mesoscale processes not resolved at coarse resolution. Present-day (1990-1999) and future (2020-2029, 2045-2054, and 2090-2099) conditions are simulated at high resolution (15-km grid spacing) using the MM5 model system and forced by ECHAM5 global simulations. Simulations use the IPCC Special Report on Emissions Scenarios (SRES) A2 emissions scenario. The mesoscale simulations produce regional alterations in snow cover, cloudiness, and circulation patterns associated with interactions between large-scale climate change and regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. Warming is significantly amplified in regions where snow cover is lost through snow-albedo feedback. Precipitation increases in autumn are amplified over topography due to changes in the large-scale circulation and its interaction with the terrain. The robustness of the modeling results is established through comparisons with the observed and simulated seasonal variability and with statistical downscaling results.
GC21A-0146
Sensitivity of the CRCM Climate to Lateral Boundary Conditions.
The Canadian Regional Climate Model (CRCM) has been used at Ouranos to generate climate simulations over North America using different experimental setups. The setups differ by the CRCM version used, the source used for the Lateral Boundary Conditions (LBC), the domain used for the simulations and the values used for different CRCM parameters. Regional Climate Models (RCMs) are often seen and used as tools to refine the large-scale information given by the LBC. In that use, it is usually assumed that the RCMs are essentially adding details to the large-scale contained in the LBC. In this study we examine this assumption by looking at the climate produced by CRCM simulations using different setups. Comparisons of the different simulated climates are presented.
GC21A-0147
Spatial Trend Patterns in Atmospheric Temperature Observations of the Microwave Sounding Unit
Although NOAA Microwave Sounding Unit (MSU) observations have been extensively used to study the global atmospheric temperature changes, spatial patterns of the MSU trend have not been fully explored. This is partly because intersatellite biases are geographic location dependent, so latitude/longitude-dependent merging techniques are required to investigate the MSU regional trend patterns. We have recently developed an intercalibration procedure based on the simultaneous nadir overpass (SNO) matchups. This intercalibration can effectively eliminate intersatellite biases and their spatial distribution. Based on this method, we have recalibrated MSU channels 2, 3, and 4 for NOAA 10, 11, 12, and 14. This presentation will provide a comprehensive report on the calibration results for these satellites. Those results include the intersatellite bias structure and statistics, bias removal techniques, and long-term trend patterns for the MSU channels 2, 3, and 4, which respectively represent the bulk layer temperature of the mid-troposphere (T2), tropopause (T3), and lower- stratosphere (T4). We will present the regional trend values and the uncertainties associated with the trends. Regions are divided into tropics, mid-latitudes, Arctic, and Antarctic. For instance, the tropical mid-troposphere (channel 4 adjusted channel 2 trend) is found to be warming at a rate of 0.280 K/decade for the period of 1987- 2006. We will discuss effects of other factors such as diurnal cycle correction and limb-correction on the trend. We also compare these trend patterns with other studies.
GC21A-0148
Detection and Attribution of Climate Change in JFM Streamflow Fractions in the Western United States
This article focuses on observed shifts in the seasonality of streamflow, and is the first in a series of papers centered on the detection and attribution of hydroclimatological parameters in the western US. The optimal detection method applied here reduces the dimensionality of a problem to a univariate or low-dimensional space. In this low-dimensional space, a detection vector is used to assess the significance of the observations for a given variable with a given climate change pattern. The results of this work indicate that observed changes in the January--March streamflow fraction in the western US are detectable and can be attributed to climate change. Detection and attribution are positive even after the El Nino-Southern Oscillation and the North Pacific Oscillation signals are factored out of the analysis. In general, we find that anthropogenic greenhouse gases and sulphate aerosols have had a detectable influence on the seasonality of streamflow over the second half of the 20th century.
GC21A-0149
Do Clouds Follow Deforestation Over the Amazon?
A statistical analysis of ten years of remote sensing observations of cloudiness from geo-stationary satellites (GOES) has produced the strongest evidence of the impact of land cover over the deforested Amazon on the development of convective clouds. Shallow clouds are prone to appear over deforested surfaces while high clouds occur over forested surface but much less frequently. A understanding of the physical mechanisms responsible for the observations is possible using simultaneous atmospheric soundings at a forest and a pasture site during the Rondonian Boundary Layer Experiment (RBLE-3). The atmospheric boundary layer over the forested area is conditionally more unstable characterized by large values of the convective available potential energy (CAPE). At the same time the boundary layer has high negative energy. The occurrence of deep convection over the forested region is then limited by available lifting mechanisms. More active shallow convection over the deforested area is caused by stronger lifting mechanisms mainly due to the mesoscale circulations driven by deforestation induced surface heterogeneities. Convection is shallow because the available convective energy is limited over the deforested region. We also found some evidence that smoke from biomass burning in the deforested area may significantly reduce the formation of shallow clouds arguably through reducing the drop sizes of cloud water.
GC21A-0150
Observed and Modeled Temperature Inversions for Fairbanks Alaska
Human populations in the Arctic are concentrated in complex orographic locations that are inadequately resolved by global climate models. Therefore, it is difficult to construct climate projections useful for humans based on GCM results only. A dynamical downscaling model approach is used over Alaska to provide high-resolution climate information, which aims to capture important local climate phenomena such as surface-based temperature inversions. The focus of this presentation is to analyze inversion characteristics using observed radiosonde data in Alaska and to evaluate how well GCM and downscaled simulations capture key characteristics of inversions. The model component of this project incorporates a multi-scale approach, using the National Center for Atmospheric Research (NCAR) Community Climate System Model Version 3 (CCSM3) and NCEP/NCAR Re- analysis for the large scale climate and the Arctic MM5, for high-resolution simulations. Downscaled Re-analysis results for the period 1994-2004 are compared to observed radiosonde data for the last half-century. These results are compared to downscaled CCSM climate. The first step in this work develops a comprehensive climatology of observed local conditions. Wintertime (October-March) surface-based inversions in Fairbanks are analyzed from 1957-2005 using radiosonde soundings in order to determine characteristics and possible connections to the large-scale climate. Additionally, inversion characteristics were analyzed to determine any trends and variability over time. During the winter months in Fairbanks, there is a high correlation between inversion depth and inversion temperature difference. When the inversion is deep the temperature difference across the inversion is large. Subsequently, surface temperature is negatively correlated to both depth and temperature difference. When surface temperatures are warm, the inversion is shallow with a small temperature difference. Concurrently, when the surface is cool, the inversion is deep and the temperature gradient is large. Additional observed characteristics of inversions such as strength and frequency will be presented and then compared to inversion characteristics from the downscaled simulations.
GC21A-0151
Temporal and spatial variations of moist enthalpy in the U.S. High Plains region
Climate change and variability involved many aspects of the climate system and the assessment of anthropogenically-forced climate change has considerably focused on surface temperature as a primary variable. In this study, authors addressed the temporal and spatial variation of heat content in surface air and moist enthalpy in the US High Plains regions by using hourly observation networks from 1985 to current, in which the hourly data sets are homogenous and quality data. We examined the temporal and spatial variations of surface temperature and heat content of surface temperature, as well as the variation of dry line between spring and summer seasons in the High Plains Region. We also compared our results with climate changes derived from daily data sets from the US Historical Climate Networks. Our assessments on the trend and variation of surface moist enthalpy provided a different view of climate change in the US High Plains Region.
GC21A-0152
Detection and Attribution of temperature changes in the mountainous western United States.
Under climate change, one of the major challenges that water managers face in the western United States is adequately meeting the water demand while minimizing the flood risk. It has been shown that, in the second half of the 20th century, winters and springs have warmed, the partition of precipitations has changed, the snow pack melts earlier and that the timing of streamflows has shifted towards the winter. A better understanding of the primary causes of these changes are crucial to reliably project future water availability. Hydrological changes can be driven by temperature or by precipitation changes, or a combination of the two. In this study, which is part of a more integrated analysis focusing on the detection and attribution of changes in the hydrological cycle, we raise the following questions: What are the causes of temperatures changes in the mountainous regions in the second half of the 20th century? Can we verify whether the observed earlier melting of snow is driven by human-induced temperature changes, rather than by changes in precipitation or natural internal climate variability? To address these questions, we conduct a detection and attribution analysis based on daily minimum and maximum temperatures, and on temperature variables that are more relevant to a potential shift in snowmelt (number of frost days and number of degree-days below 0C). We find that natural internal climate variability alone cannot explain the increase in temperature, the reduction of frost days and the decline in degree-days below 0C. External forcings agents such as the solar variability and volcanic eruptions cannot explain those changes either. Instead, we find a positive detection when the influence of anthropogenic greenhouse gases and sulphate aerosols effects are included in the climate forcings.
GC21A-0153
Structure and Origins of Trends in Hydrologic Measures over the Western US
This study investigates, at fine scale resolution, the spatial structure of observed trends in some key hydrologic measures over the western United States, and whether these trends are statistically significantly different from trends associated with natural climate variations. Measures of interest include wet day average temperatures, JFM runoff fractions of water year totals, numbers of wet day flood events, ratios of 1st April Snow Water Equivalent to cool season precipitation (SWE/Pcool), and ratios of summer soil moisture to annual precipitation (SM/Pann). The wet-day temperatures analyzed for trends were from a set of gridded daily observations usually used as inputs to the Variable Infiltration Capacity (VIC) hydrological model on a 12 km grid from 1950-1999. Those gridded observations were compared to simulated naturally occurring fluctuations from a 850 year control run of the CCSM3 FV climate model, as downscaled to the same grid. The observed and simulated meteorologies were used to drive the VIC hydrological simulations to obtain the remaining variables analyzed. Trends as large as the observed trends in several of the hydrometeorological variables--including wet day average temperatures and SM/Pann--occur less than 10% of the time in the long control run over large parts of the Western US. Although much of the trends in JFM runoff fractions can be attributed to large scale trends in cool season precipitation, there is also an elevation and temperature component to these changes that can be attributed to winter and spring warming over the western US. These runoff changes were consistent with reductions in SWE/Pcool that occurred preferentially in the same elevation and mean temperature zones, i.e., those that are most sensitive to winter and spring warming. The observed trends are different from long term fluctuations found in the natural variations in the control run and are more like those encountered in simulations forced with recent greenhouse gas increases. If these trends continue into future decades, they will have serious implications for the hydrological cycle and water supplies of the Western United States.
GC21A-0154
Methodology for Statistical Detection of Climate Change
Many detection or attribution studies are applications of the "optimal fingerprints" method, which is based on the optimization of a signal-to-noise ratio. One of the difficult point is that this method requires to know, or, in practice, to estimate the covariance matrix of the internal climate variability. In this work, a new adaptation of the "optimal fingerprints" method is presented for climate change detection. This adaptation is based on the use of a regularized estimation of the covariance matrix, that avoid to truncate it to a reduced dimension space. This technique presents two main advantages. On the one hand, under some acceptable statistical hypothesis, it can be shown to yield to a more powerful detection test. On the other hand, the covariance estimation is still efficient when the number of years used for covariance estimation is close to the number of observed time series. In order to validate the efficiency of the detection algorithm, it is first applied with pseudo-observations derived from transient regional climate change scenarios covering the 1960-2099 period. Then it is used to perform a detection study of anthropogenic climate change over France, analyzing homogenized temperature and rainfall series from 1900, produced at Météo France. The new methodology allows to estimate the covariance matrix only using part of the observation dataset. This new approach allows to confirm and reinforce the detection of an anthropogenic climate signal over the country.
GC21A-0155
Detection and Attribution of Anthropogenically Induced Changes in Snowpack Over the Western U.S.
There has been much previous work on observed changes in the hydrological cycle over the western U.S. in recent decades. These changes have significant societal impacts, since the western U.S. is generally arid and has a large and growing population that puts stress on the available water supply. In some regions, precipitation retained in mountain snowpack acts as a "natural reservoir" of water that is stored during intense winter storms and gradually released during the late spring and summer, when precipitation is generally less. Changes in the retention of winter precipitation in snow could therefore have important consequences on water availability and winter flooding, or alternatively require expensive changes in the water storage infrastructure. In this study we perform a fingerprint-based detection and attribution study to address the question of whether the observed changes in snowpack are due to natural variability, or arise from anthropogenic effects on the climate. We find that the trends in snowpack are unlikely to have arisen from natural variability alone, but are consistent with the changes expected to be seen due to anthropogenic forcing of the climate. An interesting aspect of this is that the Pacific Decadal Oscillation, a natural climate fluctuation, appears to be influenced by anthropogenic effects. While this has been implied in previous works in the literature, the consequences this has for climate over the western U.S. do not seem to have been pointed out, and must be taken into account when trying to understand the relative roles of anthropogenically forced and natural climate variability in the region.
GC21A-0156
Impact of the Northern Annular Mode on Spring Climate Patterns and Ecosystem Response in the Western United States
Many climate models project increases in the Northern Annular Mode (NAM) and an associated northward shift in the storm track over the 21st century. Here we show that the Pacific storm track is displaced to the north during springs following high index winters, and that this shift is accompanied by a south-to-north redistribution of precipitation across the western United States. In addition, higher NAM indices are associated with warmer temperatures, particularly in the Southwest. The combination of decreasing precipitation and warmer temperatures commonly signals the start of spring throughout much of the West. An earlier spring onset may have significant impacts on ecological systems, and spring NDVI is, in fact, reduced over much of the West following high index winters. This suggests that a better understanding of projected changes in the seasonality of climate will be necessary for predicting ecological impacts and may aid in identifying climate shifts attributable to human activity.
GC21A-0157
Change in climate and nature over Toyama prefecture due to global warming
Toyama prefecture is located in the center of the mainland of Japan and is surrounded by steep mountains called Tateyama at about 3,000m above sea level and by the deep Toyama bay at about 1,000m depth. In summer, since Pacific high covers mainland of Japan and East Asian summer monsoon brings a lot of rainfalls, climate in Toyama is suitable for cropping the highest qualified rice and vegetables. In winter, the dominant East Asian winter monsoon brings water from the Japan Sea by heavy snowfalls onto Tateyama. As the snow melts gradually from spring to early autumn, the abundant pure water is utilized for generating hydroelectric power and for a variety of other purposes, making it a vital resource for industries, agriculture, fishery, and human life as well as for wildlife on both sides of the plains and the mountains of Toyama. In recent, by the IPCC-AR4, influence of global warming is reported in many aspects of nature and human lives all over the world. However, we have yet to realize whether these signs are also appeared in Toyama. Therefore, we carried out statistical analyses to investigate change of nature, climate, and human lives in Toyama by global warming. Some of main results are as follows. Using the phenological data of a sample maple (Acer palmatum) tree growing at the garden in Toyama Local Meteorological Observatory, we analyzed that the leaf-color-change date is delayed ca. 20 days and the leaf-falling date is delayed ca. 10 days during recent 30 years. Using daily snowfall data between 1958 and 2007, we found that snow amounts and snowfall days are decreased significantly on the plains, while there is no trend on the mountain side. Using AMeDAS's hourly temperature data between 1978 and 2006, we detected increases in winter time minimum temperature, summer time maximum temperature, and "typical summer days" which is defined as total days that the daily maximum temperature exceeds 30 degree C. It can be inferred from these findings that the climate change over Toyama is occurring with no doubt, and it is necessary for us to make plans for adapting to the future change. Now, we are conducting future climate simulations of Toyama by downscaling some SRES scenario data. We will show some preliminary results of the simulation at the meeting.
GC21A-0158
Validation Study for Precipitation and Surface Temperature in Mexico and Their Estimation for the XXI Century
Nowadays it is known that in order to estimate whatever climate change impact for a given region, it is necessary to work with the results of the ensemble of coupled GCM simulations, especially those that recently participated for the IPCC 4th Assessment Report. However, it is also a known fact that we will have better confidence on those model estimations for the future if we can observe that they are able to reproduce at least the main large scale features of the present-day climatology. A validation study is performed here for the results of that ensemble of models in Mexico in the case of surface temperature and precipitation during the period 1961-1990. We compare the model results with the Climate Research Unit databases for that period, and check whether the models are able or not to reproduce the main large-scale features of precipitation and surface temperature around Mexico. Based on the above results we analyze the XXI Century large time simulations of the ensemble of 23 models for the SRES-A1B and SRES- A2 scenarios and try to look for possible spatial and temporal variations in precipitation and surface temperature in the region. We are also currently working with another approach of making the ensemble of models, the Reliability Ensemble Averaging (REA) method of Giorgi and Mearns (2002), instead of just taking the simple model average. The REA method takes into account the ability of the model in reproducing present-day climate and the convergence of the simulated changes across models to make the ensemble. We will present preliminary results of the comparison between both the simple-average and the REA methods for Mexico.
GC21A-0159
Trends In Wintertime Climate Variability In The Northeastern United States: 1970- 2004
Humans experience climate variability and climate change primarily through changes in weather at a local and regional scale. One of the most effective means to track these changes is through detailed analysis of meteorological data. In this work, changes in the winter climate of the northeastern United States are documented. Snow on the ground and snowfall are important components in water management, travel safety, and winter tourism and recreation. Trends in Temperature, snowfall, and snow depth data were collected from the United States Historical Climate Network (USHCN). The months of December through March are selected for winter climate analysis. Monthly and seasonal time series of the number of days with snow on the ground greater than 1, 3, and 5 inches are constructed from snow depth data. The National Climatic Data Center and Carbon Dioxide Information Analysis Center perform extensive quality assurance and quality control measures for monthly temperature data. However, daily snowfall and snow depth data have not been adjusted for station relocations, instrument changes, or time of observation biases. To address these data quality issues, we evaluate daily data for spatial coherence with nearest neighbors, and remove stations with non-climatic influences from regional analysis. Monthly and seasonal trends in mean, minimum and maximum temperature, total snowfall, and days with snow on the ground are estimated using linear regression and robust spline analysis. Northeastern United States winter temperatures are warming at a rate significantly greater than the global average. At stations located north of 44oN, December snowfall exhibits a decreasing trend (-3.5 inches/decade), whereas March snowfall is increasing (+1.3 inches/decade) over the period 1970-2004. Across the northeastern United States, the number of days with snow on the ground has also decreased substantially. The results hold important implications for the winter economy and recreation in the region.
GC21A-0160
Tendency and Long Memory Detection in Precipitation Indexes in Tuscany
The climate change issues are becoming everyday more central, not only for scientist and specialist but for a large part of the public opinion. An important issue involved in this science is covered by the modification of precipitation regime, since droughts and water resources management are great problems in several countries. Citizens, stakeholders and managers have to know if the amount and the distribution of the available water could be different in the future. The analyses of climatic events like long periods of water shortage are made more difficult by the general lack of long sequences of data. Here the authors analyzed 5 indexes of precipitation regime: the annual precipitation, the number of wet days (precipitation > 1 mm), the Precipitation Concentration Index PCI, the number of days with more than 10 mm of precipitation and the maximum number of consecutive dry days (precipitation < 1 mm). The region analyzed is the Tuscany with a dataset of 785 rain gauges, cover the period 1916-2003. A methodology to use more data than usual, including the gauges with very short time series, even only 1 year, is purposed, basing on time variable spatial interpolation techniques. Both a distributed and lumped trends analysis of the indexes calculated has been performed by mean of the Mann-Kendall test. The time series of regional value of the monthly precipitation and monthly number of wet days has been detected to present long memory, i.e. to reveal the presence of a not negligible dependence between distant observations in the time series. The implication of long memory or long term persistency LTP can led to a dramatic increase of uncertainty in statistical estimation. The results do not show any evident signals of changes in the amount of water precipitated in Tuscany during the last century even in the more restrictive hypothesis of absence of long term persistency.
GC21A-0161
Warming Asymmetries due to Surface Turbulent Heat Flux Feedbacks in IPCC AR4 Climate Simulations
We use the newly developed Climate Feedback Response Analysis Model (CFRAM) to evaluate the coupled atmosphere-surface temperature changes due to the surface latent and sensible heat flux feedbacks in IPCC AR4 climate simulations. The CFRAM enables us to examine the warming patterns due to both feedbacks that directly affect the TOA radiative fluxes and feedbacks that do not, such as evaporation and surface sensible flux feedbacks. We estimate the surface flux feedback patterns from the difference between the 2×CO2 and control experiments. The temperature changes attributable to the surface sensible heat flux feedback are positive over the ocean with maximum values over the southern ocean and northern hemisphere gulf currents. Over land, the surface sensible heat flux feedback causes a reduction of surface warming over southern Africa, the northeastern part of South America and around the northern hemisphere mid-latitudes, corresponding to a negative feedback. The temperature response attributable to the evaporation feedback is a warming over land with the exception of the equatorial Africa and the mid to high latitudes of the northern hemisphere. Over oceans, the evaporation feedback is negative, causing a reduction of sea surface warming except over the equatorial Pacific Ocean where the evaporation feedback is positive. Overall, the effects of evaporation and surface sensible flux feedbacks tend to cancel one another. The net temperature change patterns in response to total surface flux feedbacks are positive in the mid-latitudes, Arctic region, and equatorial Pacific, but negative in sub-tropics and over Antarctica.
GC21A-0162
Scalar Prediction in Climate Forecasting Using Satellite Data
Scalar detection in climate change research, having taken the form of optimal detection/linear multi-pattern regression, has been used in the recent past to detect multiple climate signals in the presence of natural inter- annual variability and associate those signals with specific causes. It has been applied to many climate observables to show high probabilities of human influence on climatic trends. One of the sources of uncertainty and instability in this methodology concerns the degree to which one can trust the fine details of a signal's shape in using it as a fingerprint associated with forced climate change. In a recent paper by Huntingford et al.~(2006), this problem has been largely solved using multi-model ensemble simulations of signal shapes to ascertain the degree to which details of signal shapes can be trusted. We show that this method, when generalized in the context of Bayesian inference, is a powerful tool—--one that carefully incorporates the scientific method—--for predicting arbitrary scalar trends in the climate system that optimally considers both observed trends and ensemble model prediction of those trends. In this method, arbitrary but informative data sets with credible trends can be used in conjunction with a large ensemble of disparate climate models to forecast anything from regional trends in temperature, humidity, cloud-cover, and precipitation to global scale trends in surface air temperature or widening of the Hadley circulation. The method weights data by inter-annual variability and connects arbitrary data sets to scalar quantities of interest according to the certainty of the physics that relates the data type to the quantities. Depending on the data set and geophysical variable of interest, forecast accuracy for that variable can be improved by large factors over simple trending of past measurements of that variable. We will present a Bayesian derivation of this methodology and give several illustrative examples for its application.
GC21A-0163
Oceanic Influences on Recent Continental Warming
Evidence will be presented that the recent worldwide warming of the continents has occurred largely in response to a worldwide warming of the oceans rather than as a direct response to increases of greenhouse gases (GHGs) over the continents. Atmospheric model simulations of the last half-century with prescribed observed ocean temperature changes, but without prescribed GHG changes, account for most of the continental warming. The oceanic influence has occurred through hydrodynamic-radiative teleconnections, primarily by moistening the air over the continents and increasing the downward longwave radiation at the surface. The oceans may themselves have warmed from a combination of natural and anthropogenic influences, as suggested by substantial differences between the observed recent warming trend and the ensemble-mean warming trend simulated by the IPCC models with all the radiative forcings included. http://www.cdc.noaa.gov/people/gilbert.p.compo/CompoSardeshmukh2007.pdf
GC21A-0164
Detection of an Emerging Anthropogenic Warming Signal at Regional Scales
Previous studies have shown that observed significant warming trends over the period of 1950 and 1999 in surface air temperature consistent with the response to anthropogenic forcing are detected at scales on the order of 500 km in many regions of the globe. In this study, we investigate the emerging anthropogenic warming signals in the scenario future runs simulated by six AR4 models at regional scales. Simulated trends in surface temperature over different periods starting from the year of 2000 at individual 5x5 latitude and longitude grid boxes are compared with model estimates of the natural internal variability of these trends. It is found that an emerging anthropogenic warming signal is detected as early as the year of 2015 at continental scales, and the year of 2020 at large fractions of the grid boxes over the globe. Detection results here are likely to be of considerable practical importance, as natural and human systems are more likely to be affected by regional temperature changes when these changes are outside the range normally experienced by the systems.
GC21A-0165
A METHOD FOR ESTIMATING THRESHOLD CROSSING TIMES WITH AN APPLICATION TO CLIMATE CHANGE
Climate projections for the 21st century show that many subtropical regions, including the North American Southwest and the Mediterranean Basin countries are facing an imminent drying tendency. In the near-term, this drying is projected to proceed at a rate that does not depend strongly on the exact greenhouse gas emission scenario. It is important for decisions makers to assess the time when a given amount of drying will occur the associated limits of uncertainty. This can be done using a multi-model ensemble. Here we compare the more common method of finding such threshold crossing time ? which is based on following a multi-model average ? against an alternative, more accurate method that makes use of the special properties of estimators of the expectation. The method consists of calculating the threshold crossing time for all models (and ensemble members) participating in the assessment and looking at the various moments of the crossing-time distribution. We show that the alternative method is superior in its prediction capabilities, generates more accurate results, is very consistent, and gives more meaningful results including a timing error estimate.
GC21A-0166
The Asymmetry between Trends in Spring and Autumn Temperature and Circulation Regimes over Western North America
Observational evidence shows that spring temperatures over western North America have undergone significant warming over the past half century, while autumn temperatures have shown relatively little change. Low- frequency modes of atmospheric variability for spring and autumn are demonstrated to account for a great deal of the seasonal asymmetry, with trends in spring circulation patterns exacerbating regional warming, and trends in autumn circulation patterns counteracting warming. After excluding warming associated with the primary modes of atmospheric variability, temperature trends in spring and autumn over western North America are similar to one another and in broad agreement with seasonal trends from a multimodel ensemble.