The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models

Abstract. Weather and climate models have improved steadily over time as witnessed by objective skill scores, although significant model errors remain. Given these imperfect models, predictions might be improved by combining them dynamically into a so-called supermodel. In this paper a new training scheme to construct such a supermodel is explored using a technique called cross pollination in time (CPT). In the CPT approach the models exchange states during the prediction. The number of possible predictions grows quickly with time, and a strategy to retain only a small number of predictions, called pruning, needs to be developed. The method is explored using low-order dynamical systems and applied to a global atmospheric model. The results indicate that the CPT training is efficient and leads to a supermodel with improved forecast quality as compared to the individual models. Due to its computational efficiency, the technique is suited for application to state-of-the art high-dimensional weather and climate models.

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Ensemble climate predictions using climate models and observational constraints

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2007.2075 ◽

2007 ◽

Vol 365 (1857) ◽

pp. 2029-2052 ◽

Cited By ~ 44

Author(s):

Peter A Stott ◽

Chris E Forest

Keyword(s):

Climate Change ◽

General Circulation ◽

Climate Changes ◽

Climate Models ◽

State Of The Art ◽

General Circulation Models ◽

Computationally Efficient ◽

Temperature Changes ◽

Large Ensembles ◽

Past Climate Change

Two different approaches are described for constraining climate predictions based on observations of past climate change. The first uses large ensembles of simulations from computationally efficient models and the second uses small ensembles from state-of-the-art coupled ocean–atmosphere general circulation models. Each approach is described and the advantages of each are discussed. When compared, the two approaches are shown to give consistent ranges for future temperature changes. The consistency of these results, when obtained using independent techniques, demonstrates that past observed climate changes provide robust constraints on probable future climate changes. Such probabilistic predictions are useful for communities seeking to adapt to future change as well as providing important information for devising strategies for mitigating climate change.

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Global observation-based climatology of precipitation occurrence and peak intensity

10.5194/egusphere-egu2020-7837 ◽

2020 ◽

Author(s):

Hylke Beck ◽

Seth Westra ◽

Eric Wood

Keyword(s):

Land Surface ◽

Regression Models ◽

Cross Validation ◽

Climate Models ◽

Daily Precipitation ◽

State Of The Art ◽

Coefficient Of Determination ◽

Peak Intensity ◽

Uncertainty Estimates ◽

Fold Cross Validation

We introduce a unique set of global observation-based climatologies of daily precipitation (P) occurrence (related to the lower tail of the P distribution) and peak intensity (related to the upper tail of the P distribution). The climatologies were produced using Random Forest (RF) regression models trained with an unprecedented collection of daily P observations from 93,138 stations worldwide. Five-fold cross-validation was used to evaluate the generalizability of the approach and to quantify uncertainty globally. The RF models were found to provide highly satisfactory performance, yielding cross-validation coefficient of determination (R2) values from 0.74 for the 15-year return-period daily P intensity to 0.86 for the >0.5 mm d-1 daily P occurrence. The performance of the RF models was consistently superior to that of state-of-the-art reanalysis (ERA5) and satellite (IMERG) products. The highest P intensities over land were found along the western equatorial coast of Africa, in India, and along coastal areas of Southeast Asia. Using a 0.5 mm d-1 threshold, P was estimated to occur 23.2 % of days on average over the global land surface (excluding Antarctica). The climatologies including uncertainty estimates will be released as the Precipitation DISTribution (PDIST) dataset via www.gloh2o.org/pdist. We expect the dataset to be useful for numerous purposes, such as the evaluation of climate models, the bias correction of gridded P datasets, and the design of hydraulic structures in poorly gauged regions.

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Assessing the Risk of Persistent Drought Using Climate Model Simulations and Paleoclimate Data

Journal of Climate ◽

10.1175/jcli-d-12-00282.1 ◽

2014 ◽

Vol 27 (20) ◽

pp. 7529-7549 ◽

Cited By ~ 134

Author(s):

Toby R. Ault ◽

Julia E. Cole ◽

Jonathan T. Overpeck ◽

Gregory T. Pederson ◽

David M. Meko

Keyword(s):

Climate Models ◽

Climate Model ◽

State Of The Art ◽

Global Climate ◽

Global Climate Models ◽

Mitigation Strategies ◽

Drought Risk ◽

Climate Model Simulations ◽

Projected Changes ◽

Paleoclimate Data

Abstract Projected changes in global rainfall patterns will likely alter water supplies and ecosystems in semiarid regions during the coming century. Instrumental and paleoclimate data indicate that natural hydroclimate fluctuations tend to be more energetic at low (multidecadal to multicentury) than at high (interannual) frequencies. State-of-the-art global climate models do not capture this characteristic of hydroclimate variability, suggesting that the models underestimate the risk of future persistent droughts. Methods are developed here for assessing the risk of such events in the coming century using climate model projections as well as observational (paleoclimate) information. Where instrumental and paleoclimate data are reliable, these methods may provide a more complete view of prolonged drought risk. In the U.S. Southwest, for instance, state-of-the-art climate model projections suggest the risk of a decade-scale megadrought in the coming century is less than 50%; the analysis herein suggests that the risk is at least 80%, and may be higher than 90% in certain areas. The likelihood of longer-lived events (>35 yr) is between 20% and 50%, and the risk of an unprecedented 50-yr megadrought is nonnegligible under the most severe warming scenario (5%–10%). These findings are important to consider as adaptation and mitigation strategies are developed to cope with regional impacts of climate change, where population growth is high and multidecadal megadrought—worse than anything seen during the last 2000 years—would pose unprecedented challenges to water resources in the region.

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An Evaluation of Decadal Probability Forecasts from State-of-the-Art Climate Models

Journal of Climate ◽

10.1175/jcli-d-12-00485.1 ◽

2013 ◽

Vol 26 (23) ◽

pp. 9334-9347 ◽

Cited By ~ 21

Author(s):

Emma B. Suckling ◽

Leonard A. Smith

Keyword(s):

Climate Models ◽

State Of The Art ◽

Simulation Models ◽

Empirical Models ◽

Regular Component ◽

Climate Services ◽

Global Mean Temperature ◽

Operational Forecasts ◽

Earth’S Climate ◽

Global Mean

While state-of-the-art models of Earth's climate system have improved tremendously over the last 20 years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the skill of these models not only with each other but also with empirical models can reveal the space and time scales on which simulation models exploit their physical basis effectively and quantify their ability to add information to operational forecasts. The skill of decadal probabilistic hindcasts for annual global-mean and regional-mean temperatures from the EU Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project is contrasted with several empirical models. Both the ENSEMBLES models and a “dynamic climatology” empirical model show probabilistic skill above that of a static climatology for global-mean temperature. The dynamic climatology model, however, often outperforms the ENSEMBLES models. The fact that empirical models display skill similar to that of today's state-of-the-art simulation models suggests that empirical forecasts can improve decadal forecasts for climate services, just as in weather, medium-range, and seasonal forecasting. It is suggested that the direct comparison of simulation models with empirical models becomes a regular component of large model forecast evaluations. Doing so would clarify the extent to which state-of-the-art simulation models provide information beyond that available from simpler empirical models and clarify current limitations in using simulation forecasting for decision support. Ultimately, the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate; their direct comparison provides information on progress toward that goal, which is not available in model–model intercomparisons.

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A new global reconstruction of temperature changes at the Last Glacial Maximum

Climate of the Past ◽

10.5194/cp-9-367-2013 ◽

2013 ◽

Vol 9 (1) ◽

pp. 367-376 ◽

Cited By ~ 155

Author(s):

J. D. Annan ◽

J. C. Hargreaves

Keyword(s):

Last Glacial Maximum ◽

Climate Models ◽

State Of The Art ◽

Glacial Maximum ◽

Proxy Data ◽

Temperature Changes ◽

Last Glacial ◽

The Last Glacial Maximum ◽

Global Mean ◽

The Last Glacial

Abstract. Some recent compilations of proxy data both on land and ocean (MARGO Project Members, 2009; Bartlein et al., 2011; Shakun et al., 2012), have provided a new opportunity for an improved assessment of the overall climatic state of the Last Glacial Maximum. In this paper, we combine these proxy data with the ensemble of structurally diverse state of the art climate models which participated in the PMIP2 project (Braconnot et al., 2007) to generate a spatially complete reconstruction of surface air (and sea surface) temperatures. We test a variety of approaches, and show that multiple linear regression performs well for this application. Our reconstruction is significantly different to and more accurate than previous approaches and we obtain an estimated global mean cooling of 4.0 ± 0.8 °C (95% CI).

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The efficient global primitive equation climate model SPEEDO

Geoscientific Model Development Discussions ◽

10.5194/gmdd-2-1115-2009 ◽

2009 ◽

Vol 2 (2) ◽

pp. 1115-1155 ◽

Cited By ~ 6

Author(s):

C. A. Severijns ◽

W. Hazeleger

Keyword(s):

Climate Models ◽

Climate Model ◽

State Of The Art ◽

Horizontal Resolution ◽

Ocean Model ◽

Future Climate ◽

Wave Number ◽

Future Climate Change ◽

Change Scenario ◽

Primitive Equation

Abstract. The efficient primitive-equation coupled atmosphere-ocean model SPEEDO is presented. The model includes an interactive sea-ice and land component. SPEEDO is a global earth system model of intermediate complexity. It has a horizontal resolution of T30 (triangular truncation at wave number 30) and 8 vertical layers in the atmosphere, and a horizontal resolution of 2 degrees and 20 levels in the ocean. The parameterizations in SPEEDO are developed in such a way that it is a fast model suitable for large ensembles or long runs on a workstation. The model has no flux correction. We compare the mean state and inter-annual variability of the model with observational fields of the atmosphere and ocean. In particular the atmospheric circulation, the mid-latitude patterns of variability and teleconnections from the tropics are well simulated. To show the model's capabilities, we performed a long control run and an ensemble experiment with enhanced greenhouse gasses. The long control run shows that the model is stable. CO2 doubling and future climate change scenario experiments show a climate sensitivity of 1.84 K W−1 m−2, which is within the range of state-of-the-art climate models. The spatial response patterns are comparable to state-of-the-art, higher resolution models. However, for very high greenhouse concentrations the parameterizations are not valid. We conclude that the model is suitable for past, current and future climate simulations and for exploring wide parameter ranges and mechanisms of variability. However, as with any model, users should be careful when using the model beyond the range of physical realism of the parameterizations and model setup.

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