Evaluation of snow extent and its variability in the Atmospheric Model Intercomparison Project (original) (raw)
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Journal of Geophysical Research, 2003
Simulations of snow-covered area (SCA) over Northern Hemisphere lands by a suite of general circulation models (GCMs) are evaluated. Results from GCM experiments submitted by an international array of research groups participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2) are compared to a data set derived primarily from visible band satellite imagery provided by the United States National Oceanic and Atmospheric Administration. At continental to hemispheric scales we find improvements over AMIP-1 models, including the elimination of temporal and spatial biases in simulations of the seasonal cycle of SCA, as well as improved simulations of the magnitude of interannual variability. At regional spatial scales, while no consistent model biases are identified over North America, regions over Eurasia are identified where models consistently either underestimate or overestimate SCA at the southern boundary of the seasonal snowpack. The region of greatest model bias is eastern Asia. While SCA biases are associated with temperature and precipitation biases, over only one region do we find a relationship between the magnitudes of SCA biases and the magnitudes of temperature and/or precipitation biases.
International Journal of Climatology, 1995
General circulation model (GCM) simulations of atmospheric circulation are more reliable than GCM simulations of temperature and precipitation. Thus, some researchers are developing empirical relations between observed atmospheric circulation and observed temperature and precipitation to translate GCM estimates of future atmospheric circulation into estimates of future regional temperature and precipitation. Developing climate-change scenarios in this manner assumes, at least, that relationships between atmospheric circulation and surface climate variables, such as temperature and precipitation, are properly simulated by GCMs.
A comparison of GCM-simulated and observed mean January and July precipitation
Global and Planetary Change, 1992
A high-resolution global precipitation climatology (developed by Legates and Willmott) is used to evaluate the simulated January and July precipitation fields of the GFDL, OSU, GISS and UKMO general circulation models (GCMs). Legates and Willmott's climatological estimates were derived from raingage observations and the gage biases were minimized. These estimates are spatially averaged to the resolution of each GCM and differences between the GCM-simulated field and the climatological averages are computed and mapped. Zonal averages for ten-degree bands also are examined. Precipitation rates along the Intertropical Convergence Zone (ITCZ) simulated by all four GCMs are considerably lower than the climatological estimates in both months. Moreover, inadequate representation of the seasonal migration and latitudinal extent of the ITCZ result in an underestimation of wet-season precipitation and an overestimation of dry-season rainfall. In the Southern Hemisphere, the January mid-latitude maximum is inadequately simulated and it is overestimated in July. Northern Hemispheric patterns generally are better simulated than those in the Southern Hemisphere. The spectrally-based GFDL model substantially overestimates polar precipitation while it is more accurately represented by the grid-point GCMs. Regional errors are commonly quite large (in many areas they exceed 2 mm day-l) which suggests cautious use of current-generation GCM prognostications for local-or regional-scale climate change studies.
Journal of the Meteorological Society of Japan, 2009
Reproducibility of land-surface air temperatures and land precipitation in the twentieth century by an atmospheric general circulation model (AGCM) MJ98 was investigated focusing on long term trends and year-to-year variability. The MJ98 model was jointly developed by the Meteorological Research Institute (MRI) and the Japan Meteorological Agency (JMA), and has a 270-km horizontal grid spacing (T42) with 30 vertical levels. Forcing the MJ98 model with observed historical sea surface temperatures (SST) and observed historical CO 2 concentrations, six-member ensemble integrations were conducted for 130 years from 1872 to 2001. Simulated land-surface air temperature and land precipitation were validated against observational data of the Climate Research Unit (CRU) from 1872 to 2001 and from 1951 to 1997, respectively. The model reproduces the observed positive trend of annual mean global average land-surface air temperature as well as decadal variability and year-to-year variability. The model simulates the observed positive trend of global average land-surface air temperature for all four seasons and the annual mean, though the magnitude is underestimated. The seasonality of the simulated trend is weak compared with that of the observation. At each grid point, the model generally reproduces positive trends of annual mean temperature over land. However, the simulated trends are underestimated especially over the middle and higher latitudes of the Northern Hemisphere, which can be partly attributed to the inability of model to simulate the increasing boreal wintertime trend of the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) over the last few decades. The model's ability to reproduce the year-to-year variability of the annual mean temperature is relatively higher in coastal regions than in inland regions. In the case of annual mean land precipitation, the model simulates the observed negative trend for the global average, its negative trend for the Northern Hemisphere, and its positive trend for the Southern Hemisphere, although these observed trends are not statistically significant. The model fails to reproduce year-to-year variability. The model generally reproduces the distribution of trend of global annual mean land precipitation, but large discrepancies between observation and simulation are found over Asia, Australia and southern Africa.
Climate Models and Their Simulation of Precipitation
Energy & Environment, 2014
Current state-of-the-art General Circulation Models (GCMs) do not simulate precipitation well because they do not include the full range of precipitationforming mechanisms that occur in the real world. It is demonstrated here that the impact of these errors are not trivial-an error of only 1 mm in simulating liquid rainfall is equivalent to the energy required to heat the entire troposphere by 0.3°C. Given that models exhibit differences between the observed and modeled precipitation that often exceed 1 mm day-1 , this lost energy is not trivial. Thus, models and their prognostications are largely unreliable.
2010
This study evaluates the intraseasonal variation of winter precipitation over the western United States in 14 coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of each model's twentieth-century climate simulation are analyzed. The focus is on the two dominant intraseasonal modes for the western U.S. precipitation: the 40-day mode and the 22-day mode. The results show that the models tend to overestimate the northern winter (November-April) seasonal mean precipitation over the western United States and Canada. The models also tend to produce overly strong intraseasonal variability in western U.S. wintertime precipitation, in spite of the overly weak tropical intraseasonal variability in most of the models. All models capture both the 40-day mode and the 22-day mode, usually with overly large variances. For the 40-day mode, models tend to reproduce its deep barotropic vertical structure and three-cell horizontal structure, but only 5 of the 14 models capture its northward propagation, and only 2 models simulate its teleconnection with the Madden-Julian oscillation in the tropical Pacific. For the 22-day mode, 8 of the 14 models reproduce its coherent northward propagation, and 9 models capture its teleconnection with precipitation in the tropical Pacific.
Global Climatological Features in a Simulation Using the CPTEC–COLA AGCM
Journal of Climate, 2002
The Center for Weather Forecasting and Climate Studies-Center for Ocean-Land-Atmosphere Studies (CPTEC-COLA) atmospheric general circulation model (AGCM) is integrated with nine initial conditions for 10 yr to obtain the model climate in an ensemble mode. The global climatological characteristics simulated by the model are compared with observational data, and emphasis is given to the Southern Hemisphere and South America. Evaluation of the model's performance is presented by showing systematic errors of several variables, and anomaly correlation and reproducibility are applied to precipitation. The model is able to simulate the main features of the global climate, and the results are consistent with analyses of other AGCMs. The seasonal cycle is reproduced well in all analyzed variables, and systematic errors occur at the same regions in different seasons. The Southern Hemisphere convergence zones are simulated reasonably well, although the model overestimates precipitation in the southern portions and underestimates it in the northern portions of these systems. The high-and low-level main circulation features such as the subtropical highs, subtropical jet streams, and storm tracks are depicted well by the model, albeit with different intensities from the reanalysis. The stationary waves of the Northern and Southern Hemispheres are weaker in the model; however, the dominant wavenumbers are similar to the observations. The energy budget analysis shows values of some radiative fluxes that are close to observations, but the unbalanced fluxes in the atmosphere and at the surface indicate that the radiation and cloud scheme parameterizations need to be improved. Besides these improvements, changes in the convection scheme and higher horizontal resolution to represent orographic effects better are being planned to improve the model's performance.
Gridded North American Monthly Snow Depth and Snow Water Equivalent for GCM Evaluation
Atmosphere-Ocean, 2003
Evaluation of snow cover in GCMs has been hampered by a lack of reliable gridded estimates of snow water equivalent (SWE) at continental scales. In order to address this gap, a snow depth analysis scheme developed by Brasnett (1999) and employed operationally at the Canadian Meteorological Centre (CMC), was applied to generate a 0.3°latitude/longitude grid of monthly mean snow depth and corresponding estimated water equivalent for North America to evaluate GCM snow cover simulations for the Atmospheric Model Intercomparison Project II (AMIP II) for the period 1979-96. Approximately 8000 snow depth observations per day were obtained from U.S. cooperative stations and Canadian climate stations for input to the analysis. The first-guess field used a simple snow accumulation, aging and melt model driven by 6-hourly values of air temperature and precipitation from the European Centre for Medium-range Weather Forecasting (ECMWF) ERA-15 Reanalysis with extensions from the Tropical Ocean Global Atmosphere (TOGA) operational data archive. The gridded snow depth and estimated SWE results agree well with available independent in situ and satellite data over mid-latitudinal regions of the continent, and the snow depth climatology exhibited several improvements over Foster and Davy (1988). The monthly snow depth and estimated SWE climatologies are available for downloading from the Canadian Cryospheric Information Network (http://www.ccin.ca). RÉSUMÉ [Traduit par la rédaction] L'absence d'estimations sur quadrillage fiables de l'équivalent en eau de la neige (ÉEN) à des échelles continentales a ralenti l'évaluation de la couverture de neige dans les MCG. Pour combler cette lacune, on a utilisé une méthode d'analyse de l'épaisseur de neige, élaborée par Brasnett (1999) et en usage fonctionnel au Centre météorologique canadien (CMC), afin de générer une grille à mailles de 0,3°en latitude/longitude de l'épaisseur de neige moyenne mensuelle et de l'équivalent en eau correspondant pour l'Amérique du Nord, en vue de l'évaluation des simulations de la couverture de neige des MCG dans le cadre du Projet de comparaison des modèles de l'atmosphère (AMIP II) pour la période de 1979 à 1996. Environ 8 000 observations par jour de l'épaisseur de neige ont été fournies par des stations collectives aux États-Unis et des stations climatologiques au Canada; elles ont servi de données d'entrée pour l'analyse. Le champ d'essai a
Climate models and their evaluation
2007
Developments in model formulation Improvements in atmospheric models include reformulated dynamics and transport schemes, and increased horizontal and vertical resolution. Interactive aerosol modules have been incorporated into some models, and through these, the direct and the indirect effect of aerosols are now more widely included Significant developments have occurred in the representation of terrestrial processes. Individual components continue to be improved via a systematic evaluation against observations and against more comprehensive models. The terrestrial processes that might significantly affect large-scale climate over the next few decades are included in current climate models. Development of the oceanic component of AOGCMs has continued. Resolution has increased and models have generally abandoned the so-called "rigid lid" treatment of the ocean surface. New physical parameterizations and numerics include true freshwater fluxes, improved river and estuary mixing schemes, and the use of positive definite advection schemes. Adiabatic isopycnal mixing schemes are now widely used. Some of these improvements have led to a reduction in the uncertainty associated with the use of less sophisticated parameterizations (e.g. virtual salt flux). Progress in developing AOGCM cryospheric components is clearest for sea ice. Almost all state-of-the-art AOGCMs now include more elaborate sea-ice dynamics and some now include several sea-ice thickness categories and relatively advanced thermodynamics. AOGCM parameterizations of terrestrial snow processes vary considerably in formulation. Systematic evaluation of snow suggests that surface tiling and sub-grid scale heterogeneity are important for simulating observations of seasonal snow cover. Few AOGCMs include ice sheet dynamics, and in all of the AOGCMs evaluated in this chapter and used in Chapter 10 for projecting climate change in the 21st Century, the permanent ice cover is prescribed. Developments in model climate simulation Although tracking changes in overall coupled model performance is still difficult, there is some evidence, based on experiments in which atmospheric GCMs are run with prescribed ocean and sea ice conditions, that the large-scale seasonal variations in a number of climatologically important fields are better simulated now than they were a decade ago. Simulation of marine low-level clouds, which are important for correctly simulating sea surface temperature and cloud feedback in a changing climate, has improved in some models. Nevertheless, errors in cloud simulation remain in many models. Some common model biases in the Southern Ocean have been identified, resulting in some uncertainty in heat uptake and transient climate response. Simulation of the thermocline, which was too thick, and the Atlantic overturning and heat transport, which were both too weak in earlier models, has been substantially improved in many models. It is likely that at least part of the improvement is due to the improvements in formulation mentioned above. Despite notable progress in developing AOGCM sea ice components, and an improved ability of some models to capture key features of sea-ice distribution and seasonality, AOGCMs have typically demonstrated only modest improvement in simulations of observed sea-ice since the TAR. The relatively slow progress can partially be explained by the fact that improving sea ice simulation requires improvements in both the atmosphere and ocean components in addition to the sea ice component itself. Since the TAR, developments in AOGCM formulation have improved the representation of large-scale variability over a wide range of timescales. The models capture the dominant extratropical patterns of variability including the Northern and Southern Annular Modes, the Pacific Decadal Oscillation, the Pacific-North American and Cold Ocean-Warm Land Patterns. AOGCMs simulate Atlantic multidecadal variability, although the relative roles of high and low latitude processes appear to differ between models. In the tropics, there has been an overall improvement in the AOGCM simulation of the spatial pattern and frequency of the El Niño-Southern Oscillation, but problems remain in simulating its seasonal phase locking and the Do Not Cite or Quote 8-4 Total pages: 99 Second-Order Draft Chapter 8 IPCC WG1 Fourth Assessment Report asymmetry between El Niño and La Niña episodes. Variability with characteristics of the Madden-Julian Oscillation is simulated in most AOGCMs, but typically too infrequently and with insufficient strength. GCMs are able to simulate extreme warm temperatures, cold air outbreaks and frost days reasonably well. Despite resolutions that are too coarse to resolve tropical cyclones, some coupled climate models can simulate the statistics of the larger-scale conditions necessary for tropical cyclone genesis. Simulation of extreme precipitation is dependent on resolution, parametrization and the thresholds chosen. In general models tend to produce too many days with weak precipitation (<10 mm day-1) and too little precipitation overall in intense events (>10 mm day-1). Given the large computing resources required by AOGCMs, Earth system models of intermediate complexity (EMICs) are widely used to study issues in past and future climate change that cannot be addressed with AOGCMs. Because of the reduced resolution of EMICs and their simplified representation of some physical processes, these models only allow inferences about very large scales. Since the TAR, EMICs have been evaluated via organised model intercomparisons which have revealed that, at large scales, EMIC results can compare well with observational data and AOGCM results. This lends support to the view that EMICS can be used to gain understanding of processes and interactions within the climate system that evolve on timescales beyond those generally accessible to GCMs. The uncertainties in long-term climate change projections can also be explored more comprehensively by using large ensembles of EMIC runs. Developments in analysis methods Since the TAR, an unprecedented effort has been initiated to make available new model results for scrutiny by scientists outside the modelling centers. Sixteen modeling groups performed a set of coordinated, standard experiments, and the resulting model output, analyzed by hundreds of researchers worldwide, forms the basis for much of the current IPCC assessment of model results. The benefits of coordinated model intercomparison include increased communication among modelling groups, more rapid identification and correction of errors, the creation of standardized benchmark calculations, and a more complete and systematic record of modelling progress. A few climate models have been tested for (and shown) skill in initial value predictions, on timescales from weather forecasting (a few days) to seasonal forecasting (annual). The skill demonstrated by models under these conditions increases confidence that they simulate some of the key processes and teleconnections in the climate system. Developments in evaluation of climate feedbacks Water vapour feedback remains the most important positive feedback in modelled climate sensitivity. Although the strength of this feedback varies among models, its overall impact on the spread of model climate sensitivities is reduced by lapse rate feedback, which tends to be anticorrelated. Several new studies indicate that modelled lower and upper tropospheric relative humidity respond to seasonal and interannual variability, volcanic induced cooling and climate trends, in a way consistent with observations. Taken together, observational and modelling evidence strongly favour a combined water vapour-lapse rate feedback of around the strength found in AOGCMs. Recent studies reaffirm that the spread of climate sensitivity estimates among models arises primarily from inter-model differences in cloud feedbacks. The shortwave impact of changes in boundary-layer clouds, and to a lesser extent mid-level clouds, constitutes the largest contributor to inter-model differences in global cloud feedbacks. The relatively poor simulation of these clouds in the present climate is a reason for some concern. The response to global warming of deep convective clouds is also a significant source of uncertainty in projections since current models predict different responses of these clouds. Observationallybased evaluation of cloud feedbacks indicate that climate models exhibit different strengths and weaknesses, and it is not yet possible to determine which estimates of the climate change cloud feedbacks are the most reliable. Despite advances since the TAR, substantial uncertainty remains in the magnitude of cryospheric feedbacks within AOGCMs. This contributes to a spread of modelled climate response, particularly in high latitudes, Do Not Cite or Quote 8-5 Total pages: 99
An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I)
Bulletin of the American Meteorological Society, 1999
Since its establishment in 1989 by the World Climate Research Programme, the Atmospheric Model Intercomparison Project (AMIP) has become the most prominent international effort devoted to the diagnosis, validation and intercomparison of global atmospheric models' ability to simulate the climate. The participating modeling groups represent virtually every atmospheric and/or climate modeling center in the world, while analysis of the results involves much of the international climate diagnostics community. The primary purpose of AMIP was, and continues to be, the comprehensive evaluation of the performance of atmospheric GCMs on climate and higher-frequency time-scales and the documentation of their systematic errors in an effort to foster the models' improvement.