Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models

Abstract. Pattern scaling is a well established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare three methods of pattern scaling for precipitation (regression, epoch difference, and a physically-based method) and evaluate which methods are “better” in particular circumstances by quantifying their robustness to interpolation/extrapolation, inter-model variations, and inter-scenario variations. Although the regression and epoch difference methods (the two most commonly used methods of pattern scaling) have better absolute performance in reconstructing the climate model output by two orders of magnitude (measured as an area-weighted root mean square error), the physically-based method shows a greater degree of robustness (less relative root-mean-square variation than the other two methods) and could be a particularly advantageous method if outstanding biases could be reduced. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion; these two patterns oppose each other in sign. Due to low signal-to-noise ratios, extrapolating RCP8.5 patterns to re- construct precipitation change in the RCP2.6 scenario results in double the error of reconstructing the RCP8.5 scenario for the regression and epoch difference methods. The methodologies discussed in this paper can help provide precipitation fields for other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.

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Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models

Geoscientific Model Development ◽

10.5194/gmd-10-1889-2017 ◽

2017 ◽

Vol 10 (5) ◽

pp. 1889-1902 ◽

Cited By ~ 5

Author(s):

Ben Kravitz ◽

Cary Lynch ◽

Corinne Hartin ◽

Ben Bond-Lamberty

Keyword(s):

Climate Model ◽

Cmip5 Models ◽

Future Climate Change ◽

Precipitation Pattern ◽

Established Method ◽

Global Mean Temperature ◽

Precipitation Response ◽

Climate Model Output ◽

Time Invariant ◽

Global Mean

Abstract. Pattern scaling is a well-established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is better in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO2-/non-CO2-forcing decomposition is applied. The methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.

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Global Climate Model Performance over Alaska and Greenland

Journal of Climate ◽

10.1175/2008jcli2163.1 ◽

2008 ◽

Vol 21 (23) ◽

pp. 6156-6174 ◽

Cited By ~ 157

Author(s):

John E. Walsh ◽

William L. Chapman ◽

Vladimir Romanovsky ◽

Jens H. Christensen ◽

Martin Stendel

Keyword(s):

Sea Level ◽

Root Mean Square ◽

Global Climate ◽

Systematic Errors ◽

The Arctic ◽

Future Climate Change ◽

Mean Square ◽

Sea Level Pressure ◽

Level Pressure ◽

Mean Square Errors

Abstract The performance of a set of 15 global climate models used in the Coupled Model Intercomparison Project is evaluated for Alaska and Greenland, and compared with the performance over broader pan-Arctic and Northern Hemisphere extratropical domains. Root-mean-square errors relative to the 1958–2000 climatology of the 40-yr ECMWF Re-Analysis (ERA-40) are summed over the seasonal cycles of three variables: surface air temperature, precipitation, and sea level pressure. The specific models that perform best over the larger domains tend to be the ones that perform best over Alaska and Greenland. The rankings of the models are largely unchanged when the bias of each model’s climatological annual mean is removed prior to the error calculation for the individual models. The annual mean biases typically account for about half of the models’ root-mean-square errors. However, the root-mean-square errors of the models are generally much larger than the biases of the composite output, indicating that the systematic errors differ considerably among the models. There is a tendency for the models with smaller errors to simulate a larger greenhouse warming over the Arctic, as well as larger increases of Arctic precipitation and decreases of Arctic sea level pressure, when greenhouse gas concentrations are increased. Because several models have substantially smaller systematic errors than the other models, the differences in greenhouse projections imply that the choice of a subset of models may offer a viable approach to narrowing the uncertainty and obtaining more robust estimates of future climate change in regions such as Alaska, Greenland, and the broader Arctic.

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A Performance Evaluation of Potential Intensity over the Tropical Cyclone Passage to South Korea Simulated by CMIP5 and CMIP6 Models

Atmosphere ◽

10.3390/atmos12091214 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1214

Author(s):

Doo-Sun R. Park ◽

Hyeong-Seog Kim ◽

Minho Kwon ◽

Young-Hwa Byun ◽

Maeng-Ki Kim ◽

...

Keyword(s):

South Korea ◽

North Pacific ◽

Western North Pacific ◽

Climate Model ◽

Cmip5 Models ◽

Mean Square ◽

Climate Model Evaluation ◽

Thermodynamic Conditions ◽

Mean Square Errors ◽

North Pacific Basin

Potential intensity (PI) is a metric for climate model evaluation of TC-related thermodynamic conditions. However, PI is utilized usually for assessing basin-wide TC-related thermodynamic conditions, and not for evaluating TC passage to a certain region. Here we evaluate model-simulated PI over the passage of TCs affecting South Korea (KOR PI) as well as the PI over the entire western North Pacific basin (WNP PI) using 25 CMIP5 and 27 CMIP6 models. In terms of pattern correlations and bias-removed root mean square errors, CMIP6 model performances for KOR PI are found to be noticeably improved over CMIP5 models in contrast to negligible improvement for WNP PI, although it is not in terms of normalized standard deviations. This implies that thermodynamic condition on the route of TCs affecting South Korea is likely better captured by CMIP6 models than CMIP5 models.

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Multivariable Integrated Evaluation of Model Performance with the Vector Field Evaluation Diagram

10.5194/gmd-2017-95 ◽

2017 ◽

Cited By ~ 1

Author(s):

Zhongfeng Xu ◽

Ying Han ◽

Congbin Fu

Keyword(s):

Vector Field ◽

Root Mean Square ◽

Climate Model ◽

Model Performance ◽

Field Evaluation ◽

Scalar Fields ◽

General Idea ◽

Evaluation Methodology ◽

Mean Square ◽

Pattern Similarity

Abstract. This paper develops a multivariable integrated evaluation (MVIE) method to measure the overall performance of climate model in simulating multiple fields. The general idea of MVIE is to group various scalar fields into a vector field and compare the constructed vector field against the observed one using the vector field evaluation (VFE) diagram. The VFE diagram was devised based on the cosine relationship between three statistical quantities: root mean square length (RMSL) of a vector field, vector field similarity coefficient, and root mean square vector deviation (RMSVD). The three statistical quantities can reasonably represent the corresponding statistics between two multidimensional vector fields. Therefore, one can summarize the three statistics of multiple scalar fields using VFE diagram and facilitate the intercomparison of model performances. The VFE diagram can illustrate how much the overall root mean square deviation of various fields is attributable to the differences in the root mean square value and how much is due to the poor pattern similarity. The MVIE method can be flexibly applied to full fields (including both the mean and anomaly) or anomaly fields depending on the application. We also propose a multivariable integrated evaluation index (MIEI) which takes the amplitude and pattern similarity of multiple scalar fields into account. The MIEI is expected to provide a more accurate evaluation of model performance in simulating multiple fields. The MIEI, VFE diagram, and commonly used statistical metrics for individual variables constitute a hierarchical evaluation methodology, which can provide a more comprehensive evaluation on model performance.

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Assessments of the north hemisphere snow cover response to 1.5 °C and 2.0 °C warming

10.5194/esd-2017-91 ◽

2017 ◽

Cited By ~ 1

Author(s):

Aihui Wang ◽

Lianlian Xu ◽

Xianghui Kong

Keyword(s):

Snow Cover ◽

Land Surface ◽

Surface Air Temperature ◽

Internal Variability ◽

Cmip5 Models ◽

Boreal Summer ◽

Surface Hydrology ◽

The North ◽

Global Mean Temperature ◽

Time Invariant

Abstract. The 2015 Paris Agreement has initialed a goal to pursue the global-mean temperature below 1.5 °C, and well below 2 °C above pre-industrial levels. As an important surface hydrology variable, the response of snow under different warming levels has not been well investigated. The community earth system model (CESM) project towards 1.5 °C and 2 °C warming targets, combined with CESM large Ensemble project (CESM-LE) brings an opportunity to address this issue. This study provides a comprehensive assessment of snow cover fraction (SCF) and snow area extent (SAE), and the associated Land Surface Air Temperature (LSAT) over North Hemisphere (NH) based on CESM-LE, CESM 1.5 °C and 2 °C projects, as well as CMIP5 historical, RCP2.6 and RCP4.5 products. Results show that the spatiotemporal variations of those modeled products are grossly consistent with the observation. The projected SAE magnitude change in RCP2.6 is comparable to that in 1.5 °C, but lower than that in 2 °C. The snow cover differences between 1.5 °C and 2 °C are prominent during the second half of 21st century. Changes in the LSAT and snow cover for 2071–2100 with respect to 1971–2000 exhibit the inconsistently spatial patterns. The contribution of increase in LSAT on the reduction of snow cover differs across seasons with the greatest in boreal autumn (49–55 %) and the lowest in boreal summer (10–16 %). The snow cover uncertainties induced by the ensemble variability show time invariant across CESM members, but increase with the warming signal among CMIP5 models. This feature reveals that the model physical parameterization plays a predominant role on the long-term snow simulations, while they are less affect by the climate internal variability.

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Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models

Earth System Dynamics ◽

10.5194/esd-12-367-2021 ◽

2021 ◽

Vol 12 (2) ◽

pp. 367-386

Author(s):

Anja Katzenberger ◽

Jacob Schewe ◽

Julia Pongratz ◽

Anders Levermann

Keyword(s):

Climate Change ◽

Global Warming ◽

Climate Models ◽

Monsoon Rainfall ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Cmip5 Models ◽

Future Climate Change ◽

Global Mean Temperature

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP6 are of interest. Here, we analyze 32 models of the latest CMIP6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with a high agreement between the models independent of the SSP if global warming is the dominant forcing of the monsoon dynamics as it is in the 21st century; the multi-model mean for JJAS projects an increase of 0.33 mm d−1 and 5.3 % per kelvin of global warming. This is significantly higher than in the CMIP5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP6 simulations largely confirm the findings from CMIP5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

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Multivariable integrated evaluation of model performance with the vector field evaluation diagram

Geoscientific Model Development ◽

10.5194/gmd-10-3805-2017 ◽

2017 ◽

Vol 10 (10) ◽

pp. 3805-3820 ◽

Cited By ~ 3

Author(s):

Zhongfeng Xu ◽

Ying Han ◽

Congbin Fu

Keyword(s):

Vector Field ◽

Root Mean Square ◽

Climate Model ◽

Model Performance ◽

Field Evaluation ◽

Scalar Fields ◽

General Idea ◽

Evaluation Methodology ◽

Mean Square ◽

Pattern Similarity

Abstract. This paper develops a multivariable integrated evaluation (MVIE) method to measure the overall performance of climate model in simulating multiple fields. The general idea of MVIE is to group various scalar fields into a vector field and compare the constructed vector field against the observed one using the vector field evaluation (VFE) diagram. The VFE diagram was devised based on the cosine relationship between three statistical quantities: root mean square length (RMSL) of a vector field, vector field similarity coefficient, and root mean square vector deviation (RMSVD). The three statistical quantities can reasonably represent the corresponding statistics between two multidimensional vector fields. Therefore, one can summarize the three statistics of multiple scalar fields using the VFE diagram and facilitate the intercomparison of model performance. The VFE diagram can illustrate how much the overall root mean square deviation of various fields is attributable to the differences in the root mean square value and how much is due to the poor pattern similarity. The MVIE method can be flexibly applied to full fields (including both the mean and anomaly) or anomaly fields depending on the application. We also propose a multivariable integrated evaluation index (MIEI) which takes the amplitude and pattern similarity of multiple scalar fields into account. The MIEI is expected to provide a more accurate evaluation of model performance in simulating multiple fields. The MIEI, VFE diagram, and commonly used statistical metrics for individual variables constitute a hierarchical evaluation methodology, which can provide a more comprehensive evaluation of model performance.

Download Full-text