Evaluating the effect of the statistical downscaling method on monthly precipitation estimates of global climate models

Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).

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An Analysis of Convectively Coupled Kelvin Waves in 20 WCRP CMIP3 Global Coupled Climate Models

Journal of Climate ◽

10.1175/2009jcli3422.1 ◽

2010 ◽

Vol 23 (11) ◽

pp. 3031-3056 ◽

Cited By ~ 48

Author(s):

Katherine H. Straub ◽

Patrick T. Haertel ◽

George N. Kiladis

Keyword(s):

Climate Models ◽

Global Climate ◽

Three Dimensional ◽

Kelvin Waves ◽

Global Climate Models ◽

Specific Humidity ◽

Tropospheric Temperature ◽

Wave Band ◽

Kelvin Wave ◽

Shallow Convection

Abstract Output from 20 coupled global climate models is analyzed to determine whether convectively coupled Kelvin waves exist in the models, and, if so, how their horizontal and vertical structures compare to observations. Model data are obtained from the World Climate Research Program’s (WCRP’s) Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset. Ten of the 20 models contain spectral peaks in precipitation in the Kelvin wave band, and, of these 10, only 5 contain wave activity distributions and three-dimensional wave structures that resemble the observations. Thus, the majority (75%) of the global climate models surveyed do not accurately represent convectively coupled Kelvin waves, one of the primary sources of submonthly zonally propagating variability in the tropics. The primary feature common to the five successful models is the convective parameterization. Three of the five models use the Tiedtke–Nordeng convective scheme, while the other two utilize the Pan and Randall scheme. The 15 models with less success at generating Kelvin waves predominantly contain convective schemes that are based on the concept of convective adjustment, although it appears that those schemes can be improved by the addition of convective “trigger” functions. Three-dimensional Kelvin wave structures in the five successful models resemble observations to a large degree, with vertically tilted temperature, specific humidity, and zonal wind anomalies. However, no model completely captures the observed signal, with most of the models being deficient in lower-tropospheric temperature and humidity signals near the location of maximum precipitation. These results suggest the need for improvements in the representations of shallow convection and convective downdrafts in global models.

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Multimodel Analysis of the Water Vapor Feedback in the Tropical Upper Troposphere

Journal of Climate ◽

10.1175/jcli3882.1 ◽

2006 ◽

Vol 19 (20) ◽

pp. 5455-5464 ◽

Cited By ~ 17

Author(s):

Ken Minschwaner ◽

Andrew E. Dessler ◽

Parnchai Sawaengphokhai

Keyword(s):

Water Vapor ◽

Relative Humidity ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Sea Surface ◽

Specific Humidity ◽

Upper Troposphere ◽

The Mean ◽

Tropical Upper Troposphere

Abstract Relationships between the mean humidity in the tropical upper troposphere and tropical sea surface temperatures in 17 coupled ocean–atmosphere global climate models were investigated. This analysis builds on a prior study of humidity and surface temperature measurements that suggested an overall positive climate feedback by water vapor in the tropical upper troposphere whereby the mean specific humidity increases with warmer sea surface temperature (SST). The model results for present-day simulations show a large range in mean humidity, mean air temperature, and mean SST, but they consistently show increases in upper-tropospheric specific humidity with warmer SST. The model average increase in water vapor at 250 mb with convective mean SST is 44 ppmv K−1, with a standard deviation of 14 ppmv K−1. Furthermore, the implied feedback in the models is not as strong as would be the case if relative humidity remained constant in the upper troposphere. The model mean decrease in relative humidity is −2.3% ± 1.0% K−1 at 250 mb, whereas observations indicate decreases of −4.8% ± 1.7% K−1 near 215 mb. These two values agree within the respective ranges of uncertainty, indicating that current global climate models are simulating the observed behavior of water vapor in the tropical upper troposphere with reasonable accuracy.

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Updated world map of the Köppen-Geiger climate classification

Hydrology and Earth System Sciences ◽

10.5194/hess-11-1633-2007 ◽

2007 ◽

Vol 11 (5) ◽

pp. 1633-1644 ◽

Cited By ~ 4011

Author(s):

M. C. Peel ◽

B. L. Finlayson ◽

T. A. McMahon

Keyword(s):

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Monthly Precipitation ◽

Climate Type ◽

Data Set ◽

Supplementary Material ◽

Global Data

Abstract. Although now over 100 years old, the classification of climate originally formulated by Wladimir Köppen and modified by his collaborators and successors, is still in widespread use. It is widely used in teaching school and undergraduate courses on climate. It is also still in regular use by researchers across a range of disciplines as a basis for climatic regionalisation of variables and for assessing the output of global climate models. Here we have produced a new global map of climate using the Köppen-Geiger system based on a large global data set of long-term monthly precipitation and temperature station time series. Climatic variables used in the Köppen-Geiger system were calculated at each station and interpolated between stations using a two-dimensional (latitude and longitude) thin-plate spline with tension onto a 0.1°×0.1° grid for each continent. We discuss some problems in dealing with sites that are not uniquely classified into one climate type by the Köppen-Geiger system and assess the outcomes on a continent by continent basis. Globally the most common climate type by land area is BWh (14.2%, Hot desert) followed by Aw (11.5%, Tropical savannah). The updated world Köppen-Geiger climate map is freely available electronically in the Supplementary Material Section.

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Spatial downscaling method and application of CMIP5 global climate models based on improved U-Net in Lake Poyang Basin

Journal of Lake Sciences ◽

10.18307/2022.0126 ◽

2022 ◽

Vol 34 (1) ◽

pp. 320-333

Author(s):

Li Kecheng ◽

◽

Lu Jianzhong ◽

Zhang Kerui ◽

Lu Chengyu ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Downscaling Method ◽

Lake Poyang ◽

Spatial Downscaling

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Evaluating the Performance of Land Surface Models

Journal of Climate ◽

10.1175/2008jcli2378.1 ◽

2008 ◽

Vol 21 (21) ◽

pp. 5468-5481 ◽

Cited By ~ 131

Author(s):

Gab Abramowitz ◽

Ray Leuning ◽

Martyn Clark ◽

Andy Pitman

Keyword(s):

Statistical Models ◽

Land Surface ◽

Climate Models ◽

Performance Metrics ◽

Global Climate ◽

Global Climate Models ◽

Specific Humidity ◽

Model Complexity ◽

Land Surface Models ◽

Surface Models

Abstract This paper presents a set of analytical tools to evaluate the performance of three land surface models (LSMs) that are used in global climate models (GCMs). Predictions of the fluxes of sensible heat, latent heat, and net CO2 exchange obtained using process-based LSMs are benchmarked against two statistical models that only use incoming solar radiation, air temperature, and specific humidity as inputs to predict the fluxes. Both are then compared to measured fluxes at several flux stations located on three continents. Parameter sets used for the LSMs include default values used in GCMs for the plant functional type and soil type surrounding each flux station, locally calibrated values, and ensemble sets encompassing combinations of parameters within their respective uncertainty ranges. Performance of the LSMs is found to be generally inferior to that of the statistical models across a wide variety of performance metrics, suggesting that the LSMs underutilize the meteorological information used in their inputs and that model complexity may be hindering accurate prediction. The authors show that model evaluation is purpose specific; good performance in one metric does not guarantee good performance in others. Self-organizing maps are used to divide meteorological “‘forcing space” into distinct regions as a mechanism to identify the conditions under which model bias is greatest. These new techniques will aid modelers to identify the areas of model structure responsible for poor performance.

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Empirical values and assumptions in the convection of numerical models

10.5194/gmd-2021-61 ◽

2021 ◽

Author(s):

Anahí Villalba-Pradas ◽

Francisco J. Tapiador

Keyword(s):

Climate Models ◽

Prediction Models ◽

Numerical Models ◽

Global Climate ◽

Weather Prediction ◽

Global Climate Models ◽

Spatial And Temporal Scales ◽

Weather Events ◽

Wide Range ◽

Convection Schemes

Abstract. Convection influences climate and weather events over a wide range of spatial and temporal scales. Therefore, accurate predictions of the time and location of convection and its development into severe weather are of great importance. Convection has to be parameterized in Numerical Weather Prediction models, Global Climate Models, and Earth System Models (NWPs, GCMs, and ESMs) as the key physical processes occur at scales much lower than the model grid size. The convection schemes described in the literature represent the physics by simplified models that require assumptions about the processes and the use of a number of parameters based on empirical values. The present paper examines these choices and their impacts on model outputs and emphasizes the importance of observations to improve our current understanding of the physics of convection.

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Updated world map of the Köppen-Geiger climate classification

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-439-2007 ◽

2007 ◽

Vol 4 (2) ◽

pp. 439-473 ◽

Cited By ~ 1028

Author(s):

M. C. Peel ◽

B. L. Finlayson ◽

T. A. McMahon

Keyword(s):

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Monthly Precipitation ◽

Land Area ◽

Climate Type ◽

Data Set ◽

Global Data

Abstract. Although now over 100 years old, the classification of climate originally formulated by Wladimir Köppen and modified by his collaborators and successors, is still in widespread use. It is widely used in teaching school and undergraduate courses on climate. It is also still in regular use by researchers across a range of disciplines as a basis for climatic regionalisation of variables and for assessing the output of global climate models. Here we have produced a new global map of climate using the Köppen-Geiger system based on a large global data set of long-term monthly precipitation and temperature station time series. Climatic variables used in the Köppen-Geiger system were calculated at each station and interpolated between stations using a two-dimensional (latitude and longitude) thin-plate spline with tension onto a 0.1°×0.1° grid for each continent. We discuss some problems in dealing with sites that are not uniquely classified into one climate type by the Köppen-Geiger system and assess the outcomes on a continent by continent basis. Globally the most common climate type by land area is BWh (14.2%, Hot desert) followed by Aw (11.5%, Tropical savannah). The updated world Köppen-Geiger climate map is freely available electronically at https://www.hydrol-earth-syst-sci.net/????.

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