Global simulation of precipitation

We find that at the high resolutions enabled by contemporary supercomputers, the AGCM can produce values of comparable magnitude to high quality observations. However, at the resolutions typical of the coupled general circulation models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, the precipitation return values are severely underestimated.

Very extreme weather events are important due to their potential to have serious impacts on human and ecological systems. As the mean climate changes due to anthropogenic causes, such rare events are also expected to change CCSP3. The intensity of the extreme precipitation is expected to increase with the availability of atmospheric moisture, which follows the Clausius-Clapeyron relationship Allen and Ingram To make credible predictions of future changes in extreme weather events, it is reasonable to ask a model to accurately simulate the observations of the recent past.

Precipitation is generally not as well simulated as air temperature in global climate models.

Global Forecast System (GFS)

One reason is that precipitation is influenced by vertical motions on scales smaller than the model grid. These motions, as well as relevant cloud microphysical processes, are parameterized in typical climate models. Furthermore, precipitation is often influenced by orography, which is smoothed by the finite grid sizes used in climate models. Despite these issues, climate models can have good skill in simulating large scale patterns of mean precipitation such as the zonal mean distribution.

However, models generally lack skill in accurately simulating regional distributions of precipitation and vary greatly from one model to another Covey et al. Very extreme precipitation events are well described by the statistical formalism of the Generalized Extreme Value GEV theory. The statistical character of the very extreme portion of a distribution is determined by relatively few events in samples of any reasonable size.

In the case of precipitation, these events are highly localized in both space and time due to the episodic nature of strong storms. In this paper, we investigate the ability of climate models to simulate the very extreme tails of the distribution of precipitation events. By varying the horizontal resolution of a model, we quantify the effect of better representing the local nature of individual storms on the statistics of very extreme precipitation. Duffy et al. Iorio et al.

We use the finite volume dynamics version of the model fvCAMfor our simulations Lin and Rood As used in this study, this global atmospheric model was configured according to the protocol dictated by the Atmospheric Model Intercomparison Project AMIP Gates et al.

Although the details of a stand alone atmospheric model simulation can differ from that of a fully coupled ocean atmosphere general circulation model simulation Covey et al. The first used a 2.

No model tuning specific to resolution was involved. The initial conditions were simply regridded to each of the meshes and the surface boundary conditions sea surface temperature and sea ice extent were obtained by a standard AMIP request.

Vertical resolution was unaltered and kept at the default 26 levels. Daily precipitation totals were saved and extracted from each integration. These observations are confined to the continental United States land areas and are aggregated from three sources of station rain gauge data gridded to a 0. Between 8, and 13, stations were quality controlled and gridded to about grid points using a modified Cressman scheme.As NASA weather and climate models simulate our planet on scales from hours to millennia, they produce datasets up to petabytes in size.

Such big data presents challenges to climate scientists—not to mention the government, agriculture, education, and business communities—in extracting scientific discovery and value. CDS builds tools and services that enable users to access, visualize, analyze, compare, and publish model data. We provide access to classic technologies long used in the climate science community e.

Climate modeling involves developing a computer model, integrating observational data both as initial conditions and for validation, and producing data products that are of value to multiple communities: scientific, business, and political. Climate models can be used to study climate changes ranging from a few weeks, to millennia.

global simulation of precipitation

The results identify averages and trends, not specific weather patterns. When running their mathematical simulations, the climate modelers partition the atmosphere into 3D grids. Within each grid cell, the supercomputer calculates physical climate values such as wind vectors, temperature, and humidity.

Using equations, The climate values are recalculated creating a projected climate simulation. Validating the models by comparing the results to observational data is a critical step in assessing the value of a climate model. Climate scientists also run their models using observational data for multiple decades to determine how well their model can reproduce the climate during that time period.

CDS also aims to support professionals in agriculture, urban planning, energy and water resource management, insurance, policy-making, disaster recovery, and national security planning. Using data from the last 35 years scientists are able to better examine the patterns of heat waves.

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The purpose of the NCCS is to enhance NASA capabilities in Earth Science, with an emphasis on weather and climate prediction, and to enable future scientific discoveries that will benefit humankind.

Skip to main content. Search form Search. ESGF icon. API icon. Validating the Models with Observational Data Validating the models by comparing the results to observational data is a critical step in assessing the value of a climate model.

Heat Waves Movie Using data from the last 35 years scientists are able to better examine the patterns of heat waves.We have also plotted simulated temperature for seven models and 2 scenarios. The first scenario was the 20c3m scenario which was the simulation of global temperature up to The second scenario shown for to is the a1b scenario which is usually taken to represent the projection without any major changes in the pattern of CO2 emissions.

This scenario also included a simulation of 20th century temperature leading up to the projection but a slightly different sub-set of model runs; as can seen be for the period of overlap they were very similar. We have also shown the envelope of all monthly model simulations the approximate equivalent of the yellow lines on the IPCC chart.

The main difference is that the line representing the simulations has less variation than in the IPCC graph, surprising as we use fewer models and fewer simulations. In particular the drops in temperature following volcanoes are less accentuated than in the IPCC graph. It is known that some models give more weight to volcanic aerosols than others and it may be that the models we used does not include them.

Figure 2 shows the same observed temperature but with the simulation of all 7 models. Where data were available for several simulation with a single model they were averaged for this graph. As can be seen the observed values generally fall within the range of modelled values. The most marked departure is in the s, when observed temperatures are higher than any modelled values.

By contrast the low temperature at the start of the 20th century is generally overestimated by the models. This is seen more clearly in Figure 3which shows the decadal change in simulated average of all models and observed temperature.

This graph shows that the models in general performed well from the mids to the end of the century, when they were simulating the increase in temperatures due to CO2, but less well at other times. In particular: The observed natural rate of increase from to averaged 0.

A similar observation appears to be true in relation to the start of the 21st century when the models projected a steady increase but observed values have not shown such a trend. Model Simulation of Temperature Anomoly — Europe and North America There is some doubt as to the accuracy of the global temperature anomaly data. We have also repeated the exercise for North America Figure 5which also has good long-term temperature records. In both cases the mean of the models exhibits less variance than the observed data.

Figure 4: Alternative Temperature Simulation - Europe.

Climate Data Information

Model Simulation of Global Temperature - Celcius Figures 1- 5 show the temperature expressed as the anomaly with respect to the period to In Figure 6 the simulated global temperature is expressed as degrees Celsius. In this graph we have shown the mean of all models but have not shown the observed mean as there is some uncertainty as to its exact value. The range of average temperature is from Figure 6: Global Temperature Simulation - Celcius.Notice: Normally, all AMS journal articles are freely available one year after publication date.

We hope this may be helpful to researchers and students and others in our communities who may have challenges with their usual access methods, as well as helpful to the librarians who serve them. Unfortunately custom saved searches can't be preserved; check your settings now so you can rebuild them in the new site. Because of a lack of observations, historical simulations of land surface conditions using land surface models are needed for studying variability and changes in the continental water cycle and for providing initial conditions for seasonal climate predictions.

Atmospheric forcing datasets are also needed for land surface model development. The quality of atmospheric forcing data greatly affects the ability of land surface models to realistically simulate land surface conditions. The forcing dataset was derived by combining observation-based analyses of monthly precipitation and surface air temperature with intramonthly variations from the National Centers for Environmental Prediction—National Center for Atmospheric Research NCEP—NCAR reanalysis, which is shown to have spurious trends and biases in surface temperature and precipitation.

Surface downward solar radiation from the reanalysis was first adjusted for variations and trends using monthly station records of cloud cover anomaly and then for mean biases using satellite observations during recent decades. Surface specific humidity from the reanalysis was adjusted using the adjusted surface air temperature and reanalysis relative humidity.

Surface wind speed and air pressure were interpolated directly from the 6-hourly reanalysis data. Sensitivity experiments show that the precipitation adjustment to the reanalysis data leads to the largest improvement, while the temperature and radiation adjustments have only small effects. When forced by this dataset, the CLM3 reproduces many aspects of the long-term mean, annual cycle, interannual and decadal variations, and trends of streamflow for many large rivers e.

The simulated long-term-mean freshwater discharge into the global and individual oceans is comparable to river-based observational estimates. Observed soil moisture variations over Illinois and parts of Eurasia are generally simulated well, with the dominant influence coming from precipitation. The results suggest that the CLM3 simulations are useful for climate change analysis.

It is also shown that unrealistically low intensity and high frequency of precipitation, as in most model-simulated precipitation or observed time-averaged fields, result in too much evaporation and too little runoff, which leads to lower than observed river flows. This problem can be reduced by adjusting the precipitation rates using observed-precipitation frequency maps. Corresponding author address: Dr. BoxBoulder, CO Email: tqian ucar. Historical records of surface evaporation, runoff, soil moisture, and other land surface fields are unavailable over most of the continents.

For example, there have been no direct measurements of actual evaporation or evapotranspiration over most land areas. Records of soil moisture are available only for a few regions and often are very short in length Robock et al.

Surface runoff is not measured and is often estimated using simple water balance models e. The lack of historical data of land surface fields hampers our ability to study the variability and changes in these variables and interactions among them Ziegler et al.

Moreover, these data are needed for verifying and initializing numerical weather and climate models Dirmeyer et al. For example, large biases have been identified in reanalysis evapotranspiration Lenters et al. Recently, large efforts have been devoted to simulate past land surface conditions using comprehensive land surface models forced with realistic forcing.

Mitchell et al. These multi-institutional efforts have focused on producing realistic soil moisture and other land surface fields for the recent periods for improving weather and seasonal climate forecasts.

Long-term simulations using land surface models have also been done e.The climate modeling program at GISS is primarily aimed at the development of coupled atmosphere-ocean models for simulating Earth's climate system. Primary emphasis is placed on investigation of climate sensitivity —globally and regionally, including the climate system's response to diverse forcings such as solar variability, volcanoes, anthropogenic and natural emissions of greenhouse gases and aerosols, paleo-climate changes, etc.

A major focus of GISS GCM simulations is to study the human impact on the climate as well as the effects of a changing climate on society and the environment.

This project has included simulations for the historic period, future simulations out toand past simulations for the last years, the last glacial maximum and the mid-Holocene. GCM developmental research focuses on sensitivity to parameterizations of clouds and moist convection, ground hydrology, and ocean-atmosphere-ice interactions. We have a specific focus on the climate interactions of atmospheric composition via aerosols and gas phase chemistry both as a response to climate and as a mechanism for climate change.

Climate Data Information

Ongoing field and laboratory programs in palynology, paleoclimate reconstruction, and other geophysical sciences provide fundamental climate data for evaluating model predictions. Current development is focused on the Cubed Sphere grid and dynamical core to improve the model simulations at higher resolution. The following is a list of benchmark publications for GISS global climate models in use during the past two decades. Schmidt, G.

Ruedy, J. Hansen, I. Aleinov, N.

global simulation of precipitation

Bell, M. Bauer, S. Bauer, B. Cairns, V. Canuto, Y. Cheng, A. Del Genio, G. Faluvegi, A. Friend, T. Hall, Y. Hu, M. Kelley, N. Kiang, D.

Future Temperature and Precipitation Projections

Koch, A. Lacis, J. Lerner, K. Lo, R. Miller, L. Nazarenko, V. Oinas, J. Perlwitz, Ju. Perlwitz, D. Rind, A. Romanou, G.Fly alongside NASA satellites and view real-time datasets in an immersive, 3D visualization for your desktop. Go backward and forward in time with this interactive visualization that illustrates how the Earth's climate has changed in recent history. Explore the sentinels of climate change with this interactive global ice viewer.

Go on a webquest to learn how NASA helps study and protect the land, air, water, and ice. Climate Time Machine Go backward and forward in time with this interactive visualization that illustrates how the Earth's climate has changed in recent history.

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Eyes on the Earth. Global Ice Viewer. Earth's ice cover is shrinking. See how climate change has affected glaciers, sea ice, and continental ice sheets.Climate Interactive creates interactive, easy-to-use, and scientifically rigorous simulations that enable people to see connections, play out scenarios, and see what works to address the biggest challenges we face. Our simulations are built using the long tradition of system dynamics modeling out of the Massachusetts Institute of Technology.

All of our simulations are created out of a common set of principles, and are:. Explore a solutions simulator that models policies for energy, transportation, land use, and new technologies to limit climate change.

Compare different national agricultural policy scenarios, changing land-use, livestock, crop practices, and more. This approach comes from MIT Sloan School of Management where these types of interactive exercises are used to train top business leaders. Our workshops and games have been used with a wide range of audiences from heads of state to middle school students, and can be run both in-person and using online platforms. With a group, explore climate solutions — solar, forests, carbon pricing, electric cars and more — in the En-ROADS simulator.

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global simulation of precipitation

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