Mechanism of tropical low-cloud response to surface warming using weather and climate simulations

Abstract. Recent studies demonstrate that weather and climate predictions potentially improve by dynamically combining different models into a so called "supermodel". Here we focus on the weighted supermodel – the supermodel's time derivative is a weighted superposition of the time-derivatives of the imperfect models, referred to as weighted supermodeling. A crucial step is to train the weights of the supermodel on the basis of historical observations. Here we apply two different training methods to a supermodel of up to four different versions of the global atmosphere-ocean-land model SPEEDO. The standard version is regarded as truth. The first training method is based on an idea called Cross Pollination in Time (CPT), where models exchange states during the training. The second method is a synchronization based learning rule, originally developed for parameter estimation. We demonstrate that both training methods yield climate simulations and weather predictions of superior quality as compared to the individual model versions. Supermodel predictions also outperform predictions based on the commonly used Multi-Model Ensemble (MME) mean. Furthermore we find evidence that negative weights can improve predictions in cases where model errors do not cancel (for instance all models are warm with respect to the truth). In principle the proposed training schemes are applicable to state-of-the-art models and historical observations. A prime advantage of the proposed training schemes is that in the present context relatively short training periods suffice to find good solutions. Additional work needs to be done to assess the limitations due to incomplete and noisy data, to combine models that are structurally different (different resolution and state representation for instance) and to evaluate cases for which the truth falls outside of the model class.

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Drivers of the Low-Cloud Response to Poleward Jet Shifts in the North Pacific in Observations and Models

Journal of Climate ◽

10.1175/jcli-d-18-0114.1 ◽

2018 ◽

Vol 31 (19) ◽

pp. 7925-7947 ◽

Cited By ~ 11

Author(s):

Mark D. Zelinka ◽

Kevin M. Grise ◽

Stephen A. Klein ◽

Chen Zhou ◽

Anthony M. DeAngelis ◽

...

Keyword(s):

Surface Temperature ◽

North Pacific ◽

Low Level ◽

Cloud Coverage ◽

The North ◽

Low Clouds ◽

Temperature Advection ◽

Low Cloud ◽

The North Pacific ◽

Cloud Response

The long-standing expectation that poleward shifts of the midlatitude jet under global warming will lead to poleward shifts of clouds and a positive radiative feedback on the climate system has been shown to be misguided by several recent studies. On interannual time scales, free-tropospheric clouds are observed to shift along with the jet, but low clouds increase across a broad expanse of the North Pacific Ocean basin, resulting in negligible changes in total cloud fraction and top-of-atmosphere radiation. Here it is shown that this low-cloud response is consistent across eight independent satellite-derived cloud products. Using multiple linear regression, it is demonstrated that the spatial pattern and magnitude of the low-cloud-coverage response is primarily driven by anomalous surface temperature advection. In the eastern North Pacific, anomalous cold advection by anomalous northerly surface winds enhances sensible and latent heat fluxes from the ocean into the boundary layer, resulting in large increases in low-cloud coverage. Local increases in low-level stability make a smaller contribution to this low-cloud increase. Despite closely capturing the observed response of large-scale meteorology to jet shifts, global climate models largely fail to capture the observed response of clouds and radiation to interannual jet shifts because they systematically underestimate how sensitive low clouds are to surface temperature advection, and to a lesser extent, low-level stability. More realistic model simulations of cloud–radiation–jet interactions require that parameterizations more accurately capture the sensitivity of low clouds to surface temperature advection.

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Profiling and scalability of the high resolution NCEP model for weather and climate simulations

2012 19th International Conference on High Performance Computing ◽

10.1109/hipc.2012.6507526 ◽

2012 ◽

Author(s):

R Phani ◽

A. K. Sahai ◽

A. Suryachandra Rao ◽

Jeelani Smd

Keyword(s):

High Resolution ◽

Climate Simulations ◽

Weather And Climate

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A multi-model ensemble method that combines imperfect models through learning

Earth System Dynamics ◽

10.5194/esd-2-161-2011 ◽

2011 ◽

Vol 2 (1) ◽

pp. 161-177 ◽

Cited By ~ 24

Author(s):

L. A. van den Berge ◽

F. M. Selten ◽

W. Wiegerinck ◽

G. S. Duane

Keyword(s):

Climate Model ◽

Model Ensemble ◽

Promising Strategy ◽

Climate Simulations ◽

Weather And Climate ◽

Standard Parameter ◽

Climate Model Simulations ◽

Model Equations ◽

Parameter Values ◽

Better Than

Abstract. In the current multi-model ensemble approach climate model simulations are combined a posteriori. In the method of this study the models in the ensemble exchange information during simulations and learn from historical observations to combine their strengths into a best representation of the observed climate. The method is developed and tested in the context of small chaotic dynamical systems, like the Lorenz 63 system. Imperfect models are created by perturbing the standard parameter values. Three imperfect models are combined into one super-model, through the introduction of connections between the model equations. The connection coefficients are learned from data from the unperturbed model, that is regarded as the truth. The main result of this study is that after learning the super-model is a very good approximation to the truth, much better than each imperfect model separately. These illustrative examples suggest that the super-modeling approach is a promising strategy to improve weather and climate simulations.

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Attribution of extreme weather and climate events overestimated by unreliable climate simulations

Geophysical Research Letters ◽

10.1002/2015gl067189 ◽

2016 ◽

Vol 43 (5) ◽

pp. 2158-2164 ◽

Cited By ~ 35

Author(s):

Omar Bellprat ◽

Francisco Doblas-Reyes

Keyword(s):

Extreme Weather ◽

Climate Simulations ◽

Weather And Climate ◽

Climate Events

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Evaluating Models' Response Of Tropical Low Clouds to SST Forcings Using CALIPSO Observations

10.5194/acp-2018-1008 ◽

2018 ◽

Author(s):

Gregory Cesana ◽

Anthony D. Del Genio ◽

Andrew S. Ackerman ◽

Maxwell Kelley ◽

Gregory Elsaesser ◽

...

Keyword(s):

Surface Temperature ◽

Vertical Structure ◽

Climate Models ◽

Climate Model ◽

Cloud Fraction ◽

Surface Warming ◽

Cloud Radiative Effect ◽

Low Cloud ◽

Moist Processes ◽

Low Cloud Cover

Abstract. Recent studies have shown that in response to a surface warming, the marine tropical low-cloud cover (LCC) as observed by passive sensor satellites substantially decreases, therefore generating a smaller negative value of the top-of-the-atmosphere cloud radiative effect (CRE). Here we study the LCC and CRE interannual changes in response to sea surface temperature (SST) forcings in the GISS Model E2 climate model, a developmental version of the GISS Model E3 climate model, and in 12 other climate models, as a function of their ability to represent the vertical structure of the cloud response to SST change against 10 years of CALIPSO observations. The more realistic models (those that satisfy the observational constraint) capture the observed interannual LCC change quite well (ΔLCC/ΔSST = −3.49 ± 1.01 % K−1 vs. ΔLCC/ΔSSTobs = −3.59 ± 0.28 % K−1) while the others largely underestimate it (ΔLCC/ΔSST = −1.32 ± 1.28 % K−1). Consequently, the more realistic models simulate more positive shortwave feedback (ΔCRE/ΔSST = 2.60 ± 1.13 W m−2 K−1) than the less realistic models (ΔCRE/ΔSST = 0.87 ± 2.63 W m−2 K−1), in better agreement with the observations (ΔCRE/ΔSSTobs = 3.05 ± 0.28 W m−2 K−1), although slightly underestimated. The ability of the models to represent moist processes within the planetary boundary layer and produce persistent stratocumulus decks appears crucial to replicating the observed relationship between clouds, radiation and surface temperature. This relationship is different depending on the type of low cloud in the observations. Over stratocumulus regions, cloud top height increases slightly with SST, accompanied by a large decrease of cloud fraction, whereas over trade cumulus regions, cloud fraction decreases everywhere, to a smaller extent.

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Next-Generation Time Integration targeting Weather and Climate Simulations

10.5194/egusphere-egu21-2734 ◽

2021 ◽

Author(s):

Martin Schreiber

Keyword(s):

High Performance ◽

Time Integration ◽

Time Frame ◽

Time Dimension ◽

Weather Forecasts ◽

Climate Simulations ◽

Weather And Climate ◽

Super Computer ◽

Strong Scaling ◽

High Performance Computers

Running simulations on high-performance computers faces new challenges due to e.g. the stagnating or even decreasing per-core speed. This poses new restrictions and therefore challenges on solving PDEs within a particular time frame in the strong scaling case. Here, disruptive mathematical reformulations, which e.g. exploit additional degrees of parallelism also along the time dimension, gained increasing interest over the last two decades.This talk will cover various examples of our current research on (parallel-in-)time integration methods in the context of weather and climate simulations such as rational approximation of exponential integrators, multi-level time integration of spectral deferred correction (PFASST) as well as other methods.These methods are realized and studied with numerics similar to the ones used by the European Centre for Medium-Range Weather Forecasts (ECMWF). Our results motivate further investigation for operational weather/climate systems in order to cope with the hardware imposed restrictions of future super computer architectures.I gratefully acknowledge contributions and more from Jed Brown, Francois Hamon, Terry S. Haut, Richard Loft, Michael L. Minion, Pedro S. Peixoto, Nathana&#235;l Schaeffer, Raphael Schilling

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Low-level cloud feedbacks in CMIP6 models and EUREC4A observations

10.5194/egusphere-egu2020-4077 ◽

2020 ◽

Author(s):

Anna Lea Albright ◽

Sandrine Bony ◽

Jean-Louis Dufresne ◽

Jessica Vial

Keyword(s):

Time Series ◽

Global Warming ◽

Simple Model ◽

Vertical Structure ◽

Controlling Factors ◽

Cloud Feedbacks ◽

Low Level ◽

Low Cloud ◽

Profile Changes ◽

Cloud Response

How will low-level clouds respond to global warming?&#160;We approach this question by first investigating the spread of climate&#160;sensitivity and cloud feedbacks in CMIP6 models.&#160;We stratify the cloud response by circulation regime&#160;and focus in greater detail on the cloud response in tropical regimes of subsidence and weak ascent &#160;(i.e., their vertical structure in the present-day and future climate, how cloud profile changes relate to changes in cloud-controlling factors). This CMIP6 model analysis dovetails with an observational analysis of low cloud responses from the EUREC4A field campaign. We seek to employ a simple model of low cloud behavior, constrained with&#160;observations from EUREC4A and longer time series from the Barbados Cloud Observatory,&#160;to better constrain the range of low cloud behavior spanned by CMIP6 models.&#160;

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