Understanding the distribution of multi-model ensembles

2020 ◽  
Author(s):  
Bo Christiansen
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dimensional Space ◽  
Spatial Scales ◽  
Model Errors ◽  
Standard Tool ◽  
Ensemble Mean ◽  
Model Ensembles ◽  
Annual Means ◽  
Approach Zero

<p>Ensembles of model experiments have become the standard tool both in studies of climate change and in studies of prediction on many different time-scales. When analyzing such ensembles the mean of the ensemble is often interpreted as the best estimate and the spread of the ensemble as an estimate of the uncertainty.  Naively we might argue that the error of the ensemble mean would approach zero as the size of the ensemble increases. However, this argument is based on the assumption that the ensemble is centered around the observations - the truth-plus-error interpretation. A competing assumption - the indistinguishable interpretation -- holds that the observations and the models are all drawn from the same distribution.</p><p>The rationale for the truth centered interpretation is that it is the situation that would be expected after calibration of statistical models. However, for multi-model ensembles of climate models there is an increasing amount of evidence pointing towards the indistinguishable interpretation. But why should the indistinguishable interpretation hold for an ensemble that basically is a representation of our incomplete knowledge of the climate system?</p><p>Here we analyze CMIP5 ensembles focusing on three measures that separate the two interpretations: the error of the ensemble mean relative to the error of individual models, the decay of the ensemble mean error for increasing ensemble size, and the correlations of the model errors. To get more freedom in our analysis we use a simple statistical model where observations and models are drawn from distributions with different variances and which include a bias. The two interpretations can be found as limits of this more comprehensive model  for which analytical results can be found using the simplifying properties of high dimensional space (the blessing of dimensionality).</p><p>We find that the indistinguishable interpretation becomes an increasingly better assumption when the errors are based on smaller and smaller temporal and spatial scales. Building on this, we present a simple conceptual mechanism for the indistinguishable interpretation based on the assumption that the climate models are calibrated or tuned on large scale features such as, e.g., annual means or global averages.</p>

scholarly journals Understanding the Distribution of Multimodel Ensembles

2020 ◽  
Vol 33 (21) ◽  
pp. 9447-9465
Author(s):  
Bo Christiansen
Keyword(s):  
Statistical Model ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Model Errors ◽  
High Dimensions ◽  
Ensemble Mean ◽  
Temporal And Spatial ◽  
Multimodel Ensembles ◽  
Annual Means

AbstractWhen analyzing multimodel climate ensembles it is often assumed that the ensemble is either truth centered or that models and observations are drawn from the same distribution. Here we analyze CMIP5 ensembles focusing on three measures that separate the two interpretations: the error of the ensemble mean relative to the error of individual models, the decay of the ensemble mean error for increasing ensemble size, and the correlations of the model errors. The measures are analyzed using a simple statistical model that includes the two interpretations as different limits and for which analytical results for the three measures can be obtained in high dimensions. We find that the simple statistical model describes the behavior of the three measures in the CMIP5 ensembles remarkably well. Except for the large-scale means we find that the indistinguishable interpretation is a better assumption than the truth centered interpretation. Furthermore, the indistinguishable interpretation becomes an increasingly better assumption when the errors are based on smaller temporal and spatial scales. Building on this, we present a simple conceptual mechanism for the indistinguishable interpretation based on the assumption that the climate models are calibrated on large-scale features such as, e.g., annual means or global averages.

scholarly journals Repeated surveying over 6 years reveals that fine-scale habitat variables are key to tropical mountain ant assemblage composition and functional diversity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mulalo M. Muluvhahothe ◽  
Grant S. Joseph ◽  
Colleen L. Seymour ◽  
Thinandavha C. Munyai ◽  
Stefan H. Foord
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
High Altitude ◽  
Functional Diversity ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Abiotic Factors ◽  
Composition Analysis ◽  
Fine Scale

AbstractHigh-altitude-adapted ectotherms can escape competition from dominant species by tolerating low temperatures at cooler elevations, but climate change is eroding such advantages. Studies evaluating broad-scale impacts of global change for high-altitude organisms often overlook the mitigating role of biotic factors. Yet, at fine spatial-scales, vegetation-associated microclimates provide refuges from climatic extremes. Using one of the largest standardised data sets collected to date, we tested how ant species composition and functional diversity (i.e., the range and value of species traits found within assemblages) respond to large-scale abiotic factors (altitude, aspect), and fine-scale factors (vegetation, soil structure) along an elevational gradient in tropical Africa. Altitude emerged as the principal factor explaining species composition. Analysis of nestedness and turnover components of beta diversity indicated that ant assemblages are specific to each elevation, so species are not filtered out but replaced with new species as elevation increases. Similarity of assemblages over time (assessed using beta decay) did not change significantly at low and mid elevations but declined at the highest elevations. Assemblages also differed between northern and southern mountain aspects, although at highest elevations, composition was restricted to a set of species found on both aspects. Functional diversity was not explained by large scale variables like elevation, but by factors associated with elevation that operate at fine scales (i.e., temperature and habitat structure). Our findings highlight the significance of fine-scale variables in predicting organisms’ responses to changing temperature, offering management possibilities that might dilute climate change impacts, and caution when predicting assemblage responses using climate models, alone.

Uncertainty Quantification in Multi-Model Ensembles

2018 ◽  
Author(s):  
Benjamin Mark Sanderson
Keyword(s):  
Climate Change ◽  
Uncertainty Quantification ◽  
Climate Models ◽  
Climate Model ◽  
Initial Condition ◽  
Model Errors ◽  
Model Framework ◽  
Validation Data ◽  
Model Ensembles

Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.

scholarly journals A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales

2021 ◽  
Vol 14 (1) ◽  
pp. 73-90
Author(s):  
Hsi-Yen Ma ◽  
Chen Zhou ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Mark D. Zelinka ◽  
...  
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Heat Fluxes ◽  
Model Errors ◽  
Moist Processes ◽  
The Mean ◽  
Mean State

Abstract. We present a multi-year short-range hindcast experiment and its experimental design for better evaluation of both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual timescales and facilitate model development. We used the Community Earth System Model version 1 as the base model and performed a suite of 3 d hindcasts initialized every day starting at 00:00 Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the central US, precipitation and diabatic heating associated with the Madden–Julian Oscillation (MJO), and the response of precipitation, surface radiative and heat fluxes, as well as zonal wind stress to sea surface temperature anomalies associated with the El Niño–Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment because these phenomena are either a result of interannual climate variability or only happen a few times in a given year (e.g., MJO, or cloud regime types). Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model's climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated with parameterized moist processes usually develop within a few days and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

scholarly journals Projected Seasonal Changes in Large-Scale Global Precipitation and Temperature Extremes Based on the CMIP5 Ensemble

2020 ◽  
Vol 33 (13) ◽  
pp. 5651-5671 ◽  
Author(s):  
Wang Zhan ◽  
Xiaogang He ◽  
Justin Sheffield ◽  
Eric F. Wood
Keyword(s):  
Extreme Events ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Extreme Temperature ◽  
Economic Losses ◽  
Temperature And Precipitation ◽  
Extreme Precipitation Events ◽  
The Future ◽  
Precipitation Events

AbstractOver the past decades, significant changes in temperature and precipitation have been observed, including changes in the mean and extremes. It is critical to understand the trends in hydroclimatic extremes and how they may change in the future as they pose substantial threats to society through impacts on agricultural production, economic losses, and human casualties. In this study, we analyzed projected changes in the characteristics, including frequency, seasonal timing, and maximum spatial and temporal extent, as well as severity, of extreme temperature and precipitation events, using the severity–area–duration (SAD) method and based on a suite of 37 climate models archived in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Comparison between the CMIP5 model estimated extreme events and an observation-based dataset [Princeton Global Forcing (PGF)] indicates that climate models have moderate success in reproducing historical statistics of extreme events. Results from the twenty-first-century projections suggest that, on top of the rapid warming indicated by a significant increase in mean temperature, there is an overall wetting trend in the Northern Hemisphere with increasing wet extremes and decreasing dry extremes, whereas the Southern Hemisphere will have more intense wet extremes. The timing of extreme precipitation events will change at different spatial scales, with the largest change occurring in southern Asia. The probability of concurrent dry/hot and wet/hot extremes is projected to increase under both RCP4.5 and RCP8.5 scenarios, whereas little change is detected in the probability of concurrent dry/cold events and only a slight decrease of the joint probability of wet/cold extremes is expected in the future.

scholarly journals Skill in Simulating Australian Precipitation at the Tropical Edge*

2016 ◽  
Vol 29 (4) ◽  
pp. 1477-1496 ◽  
Author(s):  
Penelope Maher ◽  
Steven C. Sherwood
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Linear Independence ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Reanalysis Data ◽  
Hadley Cell ◽  
Model Errors ◽  
Skill Scores

Abstract Expansion of the tropics will likely affect subtropical precipitation, but observed and modeled precipitation trends disagree with each other. Moreover, the dynamic processes at the tropical edge and their interactions with precipitation are not well understood. This study assesses the skill of climate models to reproduce observed Australian precipitation variability at the tropical edge. A multivariate linear independence approach distinguishes between direct (causal) and indirect (circumstantial) precipitation drivers that facilitate clearer attribution of model errors and skill. This approach is applied to observed precipitation and ERA-Interim reanalysis data and a representative subset of four models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and their CMIP3 counterparts. The drivers considered are El Niño–Southern Oscillation, southern annular mode, Indian Ocean dipole, blocking, and four tropical edge metrics (position and intensity of the subtropical ridge and subtropical jet). These models are skillful in representing the covariability of drivers and their influence on precipitation. However, skill scores have not improved in the CMIP5 subset relative to CMIP3 in either respect. The Australian precipitation response to a poleward-located Hadley cell edge remains uncertain, as opposing drying and moistening mechanisms complicate the net response. Higher skill in simulating driver covariability is not consistently mirrored by higher precipitation skill. This provides further evidence that modeled precipitation does not respond correctly to large-scale flow patterns; further improvements in parameterized moist physics are needed before the subtropical precipitation responses can be fully trusted. The multivariate linear independence approach could be applied more widely for practical model evaluation.

Merged Statistical Analyses of Historical Monthly Precipitation Anomalies Beginning 1900

2010 ◽  
Vol 23 (21) ◽  
pp. 5755-5770 ◽  
Author(s):  
Thomas M. Smith ◽  
Phillip A. Arkin ◽  
Mathew R. P. Sapiano ◽  
Ching-Yee Chang
Keyword(s):  
Twentieth Century ◽  
Time Scales ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Phase Relationship ◽  
Coupled Model ◽  
Data Availability ◽  
Monthly Precipitation ◽  
Interannual Variations

Abstract A monthly reconstruction of precipitation beginning in 1900 is presented. The reconstruction resolves interannual and longer time scales and spatial scales larger than 5° over both land and oceans. Because of different land and ocean data availability, the reconstruction combines two separate historical reconstructions. One analyzes interannual variations directly by fitting gauge-based anomalies to large-scale spatial modes. This direct reconstruction is used for land anomalies and interannual oceanic anomalies. The other analyzes annual and longer variations indirectly from correlations with analyzed sea surface temperature and sea level pressure. This indirect reconstruction is used for oceanic variations with time scales longer than interannual. In addition, a method of estimating reconstruction errors is also presented. Over land the reconstruction is a filtered representation of the gauge data with data gaps filled. Over oceans the reconstruction gives an estimate of the atmospheric response to changing temperature and pressure, combined with interannual variations. The reconstruction makes it possible to evaluate global precipitation variations for periods much longer than the satellite period, which begins in 1979. Evaluations show some large-scale similarities with coupled model precipitation variations over the twentieth century, including an increasing tendency over the century. The reconstructed land and sea trends tend to be out of phase at low latitudes, similar to the out-of-phase relationship for interannual variations. This reconstruction may be used for climate monitoring, for statistical climate studies of the twentieth century, and for helping to evaluate dynamic climate models. In the future the possibility of improving the reconstruction will be explored by further improving the analysis methods and including additional data.

scholarly journals Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin

2016 ◽  
Vol 20 (10) ◽  
pp. 4237-4264 ◽  
Author(s):  
Kaniska Mallick ◽  
Ivonne Trebs ◽  
Eva Boegh ◽  
Laura Giustarini ◽  
Martin Schlerf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Water Resource Management ◽  
Large Scale ◽  
Amazon Basin ◽  
Climate Models ◽  
Spatial Scales ◽  
Rooting Depth ◽  
Combining Data ◽  
Biophysical Controls

Abstract. Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman–Monteith and Shuttleworth–Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy–atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land–surface–atmosphere exchange parameterizations across a range of spatial scales.

scholarly journals Analysis and Reduction of Systematic Errors through a Seamless Approach to Modeling Weather and Climate

2010 ◽  
Vol 23 (22) ◽  
pp. 5933-5957 ◽  
Author(s):  
G. M. Martin ◽  
S. F. Milton ◽  
C. A. Senior ◽  
M. E. Brooks ◽  
S. Ineson ◽  
...  
Keyword(s):  
Time Scales ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Full Range ◽  
Model Development ◽  
Model Performance ◽  
Systematic Errors ◽  
Wide Range

Abstract The reduction of systematic errors is a continuing challenge for model development. Feedbacks and compensating errors in climate models often make finding the source of a systematic error difficult. In this paper, it is shown how model development can benefit from the use of the same model across a range of temporal and spatial scales. Two particular systematic errors are examined: tropical circulation and precipitation distribution, and summer land surface temperature and moisture biases over Northern Hemisphere continental regions. Each of these errors affects the model performance on time scales ranging from a few days to several decades. In both cases, the characteristics of the long-time-scale errors are found to develop during the first few days of simulation, before any large-scale feedbacks have taken place. The ability to compare the model diagnostics from the first few days of a forecast, initialized from a realistic atmospheric state, directly with observations has allowed physical deficiencies in the physical parameterizations to be identified that, when corrected, lead to improvements across the full range of time scales. This study highlights the benefits of a seamless prediction system across a wide range of time scales.

scholarly journals A multi-year short-range hindcast experiment for evaluating climate model moist processes from diurnal to interannual time scales

2020 ◽  
Author(s):  
Hsi-Yen Ma ◽  
Chen Zhou ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Mark D. Zelinka ◽  
...  
Keyword(s):  
Time Scales ◽  
Short Range ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Model Errors ◽  
Moist Processes ◽  
The Mean ◽  
Mean State

Abstract. We present a multi-year short-range hindcast experiment and its experiment procedure for better evaluating both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual time scales to facilitate model development. We use the Community Earth System Model version 1 as the based model and performed a suite of 3-day long hindcasts every day starting at 00Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the Central U.S., precipitation and diabatic heating associated with the Madden-Julian Oscillation propagation, and the response of moist processes to sea surface temperature anomalies associated with the El Niño-Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment design to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment. Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model’s climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated parameterized moist processes usually develop within a few days, and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

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