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Implementing and Improving CBMZ-MAM3 Chemistry and Aerosol Modules in the Regional Climate Model WRF-CAM5: An Evaluation over the Western US and Eastern North Pacific
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Does Dynamical Downscaling Introduce Novel Information in Climate Model Simulations of Recipitation Change over a Complex Topography Region?
Current climate and future climate-warming runs with the RegCM Regional Climate Model (RCM) at 50 and 11 km-resolutions forced by the ECHAM GCM are used to examine whether the increased resolution of the RCM introduces novel information in the precipitation field when the models are run for the mountainous region of the Hellenic peninsula. The model results are inter-compared with the resolution of the RCM output degraded to match that of the GCM, and it is found that in both the present and future climate runs the regional models produce more precipitation than the forcing GCM. At the same time, the RCM runs produce increases in precipitation with climate warming even though they are forced with a GCM that shows no precipitation change in the region. The additional precipitation is mostly concentrated over the mountain ranges, where orographic precipitation formation is expected to be a dominant mechanism. It is found that, when examined at the same resolution, the elevation heights of the GCM are lower than those of the averaged RCM in the areas of the main mountain ranges. It is also found that the majority of the difference in precipitation between the RCM and the GCM can be explained by their difference in topographic height. The study results indicate that, in complex topography regions, GCM predictions of precipitation change with climate warming may be dry biased due to the GCM smoothing of the regional topography.
NASA Downscaling Project: Final Report
A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.
NASA Downscaling Project
A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA-2 reanalyses were used to drive the NU-WRF regional climate model and a GEOS-5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000-2010. The results of these experiments were compared to observational datasets to evaluate the output.
Regional Scale/Regional Climate Model Development and Its Applications at Goddard
A Regional Land-Atmosphere Climate Simulation System (RELACS) is being developed and implemented at NASA Goddard Space Flight Center. One of the major goals of RELACS is to use a regional scale model (Penn State/NCAR MM5) with improved physical processes and in particular land-related processes, to understand the role of the land surface and its interaction with convection and radiation as well as the water/energy cycles in the Indo-China/South China Sea (SCS)/China, N. America and S. America region.
Downscaling GISS ModelE Boreal Summer Climate over Africa
The study examines the perceived added value of downscaling atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June- September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2deg latitude by 2.5deg longitude and the RM3 grid spacing is 0.44deg. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by downscaling with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 downscaling somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. Downscaling improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.
Projections of Declining Surface-Water Availability for the Southwestern United States
16 of the CMIP5 models had all the data needed for this work for at least one simulation that was continuous from 1950 to 2040. Details of the models analyzed here are provided in Table S1. The model data analyzed here are available at http://strega.ldeo.columbia.edu:81/expert/home/.naomi/.AR5/.v2/.historical:rcp85/.mmm16/ a. Assessing the climatology of the models Despite increases in horizontal resolution of many models compared to their CMIP3 counterparts none of these models can adequately resolve the topography of the south west United States, such as the Sierra Nevada and Rocky Mountains and the associated orographic precipitation. This requires that caution be used when interpreting the results presented here. To assess the ability of the models to simulate the current hydroclimate, in Figure S1 we show the observed (from the Global Precipitation Climatology Centre gridded rain gauge data, (1)) monthly climatology of precipitation and the same for all the models and the multimodel mean for the California-Nevada, Colorado headwaters and Texas regions. The GPCC data uses rain gauges only and interpolates to regular grids of which we used the 1◦ by 1◦ one. Details of the data set can be found in (2). While the models apparently overestimate precipitation in California and Nevada the seasonal cycle with wet winters and dry summers is very well represented. It is also possible that the rain gauge observations are biased low by inadequately sampling the higher mountain regions. How ever the models might also be expected to underestimate orographic precipitation due to inadequate horizontal resolution. The 25 models are also too wet in the Colorado headwaters region but correctly represent the quite even distribution though the year. The bimodal distribution of precipitation in Texas, with peaks in May and September, and the absolute amounts, are well modeled but with the September peak too weak. The positive precipitation bias translates into a positive runoff bias for the Colorado headwaters as also shown in Figure S1. Here the observed runoff values are taken from simulations of the Variable Infiltration Capacity (VIC) land surface-hydrology model (3) forced by observed meteorology (5) that were conducted as part of the North American Land Data Assimilation System project phase 2 ( (NLDAS-2), http://www.emc.ncep.noaa.gov/mmb/nldas/. Runoff for California-Nevada is better simulated but there is a positive bias over Texas despite no strong precipitation bias. To check whether regional climate models better simulate P and runoff in these regions we analyzed the historical simulation with the Regional Climate Model version 3 driven by the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 available from the North American Regional Climate Change Assessment Program (http://www.narccap.ucar.edu). This model configuration retained these biases in P and runoff although they were reduced in amplitude. Given these varying biases we plot P and P − E changes in actual values but apply the simplest bias correction possible to the runoff and soil moisture values and show the modeled changes in terms of percentages of the 20th Century model climatologies. A thorough assessment of the simulation of North American climate in CMIP5 models is conducted in Sheffield at al. (North American Climate in CMIP5 Experiments. Part I: Evaluation of 20th Century Continental and Regional Climatology, manuscript submit ted to J. Climate, available at http://www.climate.noaa.gov/index.jsp?pg=./cpo pa/ mapp/cmip5 publications.html). Sheffield et al. analyze the climatology of precipitation, surface air temperature, low level winds, moisture fluxes, runoff etc. and conclude that the main features of the hydrological cycle, including characteristics of the atmospheric moisture balance and its seasonality, are captured in the CMP5 models subject to biases in total precipitation amounts. We chose to use all available models instead of selecting some and rejecting others based on an assessment of model realism. This is in accord with the suggestions of Mote et al. for CMIP3 (4) but future work needs to revisit this matter for the case of the CMIP5 ensemble.
Regional Climate Simulation Experiments with a Variable Resolution Stretched Grid GCM
The variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) GCM has been recently developed and tested in a regional climate simulation mode. The SG-approach is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step to regional climate modeling. The region of interest with a uniform about 60 km resolution used in experiments is a rectangle over the U.S. The results of one annual as well as two-month simulations for the anomalous climate event of the U.S. drought of 1988, are validated against data analysis fields and diagnostics. The efficient regional down-scaling as well as the positive impact of fine regional resolution, are obtained. The SG-concept appeared to be a promising candidate for regional and subregional climate studies and applications.
Future Climate Change Impact on Wildfire Danger Over the Mediterranean: the Case of Greece
Recent studies have shown that temperature and precipitation in the Mediterranean are expected to change, contributing to longer and more intense summer droughts that even extend out of season. In connection to this, the frequency of forest fire occurrence and intensity will likely increase. In the present study, the changes in future fire danger conditions are assessed for the different regions of Greece using the Canadian fire weather index (FWI). Gridded future climate output as estimated from three regional climate models from the Coordinated Regional Downscaling Experiment are utilized. We use three representative concentration pathways (RCPs) consisting of an optimistic emissions scenario where emissions peak and decline beyond 2020 (RCP2.6), a middle-of-the-road scenario (RCP4.5) and a pessimistic scenario, in terms of mitigation where emissions continue to rise throughout the century (RCP8.5). Based on established critical fire FWI threshold values for Greece, the future change in days with critical fire danger were calculated for different areas of Greece domains. The results show that fire danger is expected to progressively increase in the future especially in the high-end climate change scenario with southern and eastern regions of Greece expected to have up to 40 additional days of high fire danger relative to the late 20th century, on average. Crete, the Aegean Islands, the Attica region, as well as parts of Peloponnese are predicted to experience a stronger increase in fire danger.
Annual Greenland Accumulation Rates (2009-2012) from Airborne Snow Radar
Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2-6.5 gigahertz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semi-automated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009-2012. The uncertainty in these radar-derived accumulation rates is on average 14 percent. A comparison of the radarderived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and longterm mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR - Modele Atmospherique Regional for Greenland and vicinity) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.
Regional Climate Studies with Variable-Resolution Stretched-Grid GCMs
A variable resolution GCM using a global stretched grid with fine resolution over the area(s) of interest, is a viable new approach to regional and subregional climate studies and applications. It is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step in regional climate modeling. The first version of the SG-GCM based on the GEOS (Goddard Earth Observing System) GCM using a finite-difference approximation, has been developed and thoroughly tested during the last few years. Successful simulations have been performed with the SG-GCM for the anomalous regional climate events of the U.S. 1988 summer drought and 1993 summer flood. They have shown the practical feasibility of the SG-approach for regional climate modeling. The GEOS SG-DAS (Data Assimilation System) incorporating the SG-GCM has also been developed and tested. The assimilated regional fields and diagnostics are used for validating the SG-GCM regional simulations. Two new SG-GCMs are being developed. The first is the SG-version of the new NASANCAR FV-GCM (with the finite-volume (FV) dynamics), and the second is the SG-version of the new GCM with spectral-element dynamics. Both GCMs use the WAR CCM4 physics. Using these advanced numerics will provide increased computational efficiency for the new the SG-GCMs, and will allow us to employ more flexible stretching strategies beneficial for the efficient regional down-scaling. The major current developments are focused on: simulating the 1997-1999 (and beyond) ENSO cycle and related monsoonal circulations, with enhanced regional resolution; studying intraseasonal and interannual regional climate variability for the extended multiyear (AMIP-type) SG-GCM simulations; and studying the impact of ensemble integrations.
Influences of Regional Climate Change on Air Quality Across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations
Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture regional-scale changes in temperatures and precipitation. We use a regional climate model (RCM) to dynamically downscale the GCM's large-scale signal to investigate the changes in regional and local extremes of temperature and precipitation that may result from a changing climate. In this paper, we show preliminary results from downscaling the NASA/GISS ModelE IPCC AR5 Representative Concentration Pathway (RCP) 6.0 scenario. We use the Weather Research and Forecasting (WRF) model as the RCM to downscale decadal time slices (1995-2005 and 2025-2035) and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0. The regional climate change scenario is further processed using the Community Multiscale Air Quality modeling system to explore influences of regional climate change on air quality.
Comparison of simulated cloud cover with satellite obsrvations over the Western United States
Satellite imagery datasets and regional climate model results are intercompared for evaluation of model accuracy in the simulation of cloud cover. Both monthly average individual simulation times are analyzed. To provide a consistent comparison, satellite data are first mapped into the model's geographic projection, grid domain, and resolution. It is found that September 1988 monthly average cloud fraction results from the modeled simulations correspond to observations, in both spatial pattern and magnitude, with bias less than +/- 20% cloud fraction over the entire inland West. Agreement in the pattern of cloud fraction also is evident for monthly average cloud fraction in July, but there is no negative bias of 10%-30% cloud fraction in the model diagnosis of cloud cover. Correlations between the spatial distributions of model-derived and observed cloud fractions are found to exceed 0.80 for certain geographic regions of the West, and these correlations are largest over mountainous areas during summer. Case studies of a series of daily cloud cover demonstrate the ability of the model to simulate the effects of frontal passage on cloud distribution. The ability of the RegCM1 to simulate daily cloud fraction and diurnal cloud evolution is somewhat weak for the summer convective season. It is anticipated that a more recent version of the regional climate model may improve the simulation of summer season cloud cover, through changes in cloud parameterization and improvements in model resolution.
Downscaling Atmosphere-Ocean Global Climate Model Precipitation Simulations over Africa Using Bias-Corrected Lateral and Lower Boundary Conditions
A prequel study showed that dynamic downscaling using a regional climate model (RCM) over Africa improved the Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model (GISS AOGCM: ModelE) simulation of June–September rainfall patterns over Africa. The current study applies bias corrections to the lateral and lower boundary data from the AOGCM driving the RCM, based on the comparison of a 30-year simulation to the actual climate. The analysis examines the horizontal pattern of June–September total accumulated precipitation, the time versus latitude evolution of zonal mean West Africa (WA) precipitation (showing monsoon onset timing), and the latitude versus altitude cross-section of zonal winds over WA (showing the African Easterly Jet and the Tropical Easterly Jet). The study shows that correcting for excessively warm AOGCM Atlantic sea-surface temperatures (SSTs) improves the simulation of key features, whereas applying 30-year mean bias corrections to atmospheric variables driving the RCM at the lateral boundaries does not improve the RCM simulations. We suggest that AOGCM climate projections for Africa should benefit from downscaling by nesting an RCM that has demonstrated skill in simulating African climate, driven with bias-corrected SST.
Enhanced Climatic Warming in the Tibetan Plateau Due to Double CO2: A Model Study
The NCAR (National Center for Atmospheric Research) regional climate model (RegCM2) with time-dependent lateral meteorological fields provided by a 130-year transient increasing CO2 simulation of the NCAR Climate System Model (CSM) has been used to investigate the mechanism of enhanced ground temperature warming over the TP (Tibetan Plateau). From our model results, a remarkable tendency of warming increasing with elevation is found for the winter season, and elevation dependency of warming is not clearly recognized in the summer season. This simulated feature of elevation dependency of ground temperature is consistent with observations. Based on an analysis of surface energy budget, the short wave solar radiation absorbed at the surface plus downward long wave flux reaching the surface shows a strong elevation dependency, and is mostly responsible for enhanced surface warming over the TP. At lower elevations, the precipitation forced by topography is enhanced due to an increase in water vapor supply resulted from a warming in the atmosphere induced by doubling CO2. This precipitation enhancement must be associated with an increase in clouds, which results in a decline in solar flux reaching surface. At higher elevations, large snow depletion is detected in the 2xCO2run. It leads to a decrease in albedo, therefore more solar flux is absorbed at the surface. On the other hand, much more uniform increase in downward long wave flux reaching the surface is found. The combination of these effects (i.e. decrease in solar flux at lower elevations, increase in solar flux at higher elevation and more uniform increase in downward long wave flux) results in elevation dependency of enhanced ground temperature warming over the TP.