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Maik Renner, Axel Kleidon, Martyn Clark, Bart Nijssen, Marvin Heidkamp, Martin Best, and Gab Abramowitz

Abstract

The diurnal cycle of solar radiation represents the strongest energetic forcing and dominates the exchange of heat and mass of the land surface with the atmosphere. This diurnal heat redistribution represents a core of land–atmosphere coupling that should be accurately represented in land surface models (LSMs), which are critical parts of weather and climate models. We employ a diagnostic model evaluation approach using a signature-based metric that describes the diurnal variation of heat fluxes. The metric is obtained by decomposing the diurnal variation of surface heat fluxes into their direct response and the phase lag to incoming solar radiation. We employ the output of 13 different LSMs driven with meteorological forcing of 20 FLUXNET sites (PLUMBER dataset). All LSMs show a poor representation of the evaporative fraction and thus the diurnal magnitude of the sensible and latent heat flux under cloud-free conditions. In addition, we find that the diurnal phase of both heat fluxes is poorly represented. The best performing model only reproduces 33% of the evaluated evaporative conditions across the sites. The poor performance of the diurnal cycle of turbulent heat exchange appears to be linked to how models solve for the surface energy balance and redistribute heat into the subsurface. We conclude that a systematic evaluation of diurnal signatures is likely to help to improve the simulated diurnal cycle, better represent land–atmosphere interactions, and therefore improve simulations of the near-surface climate.

Open access
Suzana J. Camargo, Claudia F. Giulivi, Adam H. Sobel, Allison A. Wing, Daehyun Kim, Yumin Moon, Jeffrey D. O. Strong, Anthony D. Del Genio, Maxwell Kelley, Hiroyuki Murakami, Kevin A. Reed, Enrico Scoccimarro, Gabriel A. Vecchi, Michael F. Wehner, Colin Zarzycki, and Ming Zhao

Abstract

Here we explore the relationship between the global climatological characteristics of tropical cyclones (TCs) in climate models and the modeled large-scale environment across a large number of models. We consider the climatology of TCs in 30 climate models with a wide range of horizontal resolutions. We examine if there is a systematic relationship between the climatological diagnostics for the TC activity [number of tropical cyclones (NTC) and accumulated cyclone energy (ACE)] by hemisphere in the models and the environmental fields usually associated with TC activity, when examined across a large number of models. For low-resolution models, there is no association between a conducive environment and TC activity, when integrated over space (tropical hemisphere) and time (all years of the simulation). As the model resolution increases, for a couple of variables, in particular vertical wind shear, there is a statistically significant relationship in between the models’ TC characteristics and the environmental characteristics, but in most cases the relationship is either nonexistent or the opposite of what is expected based on observations. It is important to stress that these results do not imply that there is no relationship between individual models’ environmental fields and their TC activity by basin with respect to intraseasonal or interannual variability or due to climate change. However, it is clear that when examined across many models, the models’ mean state does not have a consistent relationship with the models’ mean TC activity. Therefore, other processes associated with the model physics, dynamical core, and resolution determine the climatological TC activity in climate models.

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Jiabao Wang, Hyemi Kim, Daehyun Kim, Stephanie A. Henderson, Cristiana Stan, and Eric D. Maloney

Abstract

We propose a set of MJO teleconnection diagnostics that enables an objective evaluation of model simulations, a fair model-to-model comparison, and a consistent tracking of model improvement. Various skill metrics are derived from teleconnection diagnostics including five performance-based metrics that characterize the pattern, amplitude, east–west position, persistence, and consistency of MJO teleconnections and additional two process-oriented metrics that are designed to characterize the location and intensity of the anomalous Rossby wave source (RWS). The proposed teleconnection skill metrics are used to compare the characteristics of boreal winter MJO teleconnections (500-hPa geopotential height anomaly) over the Pacific–North America (PNA) region in 29 global climate models (GCMs). The results show that current GCMs generally produce MJO teleconnections that are stronger, more persistent, and extend too far to the east when compared to those observed in reanalysis. In general, models simulate more realistic teleconnection patterns when the MJO is in phases 2–3 or phases 7–8, which are characterized by a dipole convection pattern over the Indian Ocean and western to central Pacific. The higher model skill for phases 2, 7, and 8 may be due to these phases producing more consistent teleconnection patterns between individual MJO events than other phases, although the consistency is lower in most models than observed. Models that simulate realistic RWS patterns better reproduce MJO teleconnection patterns.

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Catherine M. Naud, James F. Booth, Jeyavinoth Jeyaratnam, Leo J. Donner, Charles J. Seman, Ming Zhao, Huan Guo, and Yi Ming

Abstract

The clouds in Southern Hemisphere extratropical cyclones generated by the GFDL climate model are analyzed against MODIS, CloudSat, and CALIPSO cloud and precipitation observations. Two model versions are used: one is a developmental version of “AM4,” a model GFDL that will utilize for CMIP6, and the other is the same model with a different parameterization of moist convection. Both model versions predict a realistic top-of-atmosphere cloud cover in the southern oceans, within 5% of the observations. However, an examination of cloud cover transects in extratropical cyclones reveals a tendency in the models to overestimate high-level clouds (by differing amounts) and underestimate cloud cover at low levels (again by differing amounts), especially in the post–cold frontal (PCF) region, when compared with observations. In focusing only on the models, it is seen that their differences in high and midlevel clouds are consistent with their differences in convective activity and relative humidity (RH), but the same is not true for the PCF region. In this region, RH is higher in the model with less cloud fraction. These seemingly contradictory cloud and RH differences can be explained by differences in the cloud-parameterization tuning parameters that ensure radiative balance. In the PCF region, the model cloud differences are smaller than either of the model biases with respect to observations, suggesting that other physics changes are needed to address the bias. The process-oriented analysis used to assess these model differences will soon be automated and shared.

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Allison A. Wing, Suzana J. Camargo, Adam H. Sobel, Daehyun Kim, Yumin Moon, Hiroyuki Murakami, Kevin A. Reed, Gabriel A. Vecchi, Michael F. Wehner, Colin Zarzycki, and Ming Zhao

Abstract

Tropical cyclone intensification processes are explored in six high-resolution climate models. The analysis framework employs process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled. These diagnostics include budgets of column moist static energy and the spatial variance of column moist static energy, where the column integral is performed between fixed pressure levels. The latter allows for the quantification of the different feedback processes responsible for the amplification of moist static energy anomalies associated with the organization of convection and cyclone spinup, including surface flux feedbacks and cloud-radiative feedbacks. Tropical cyclones (TCs) are tracked in the climate model simulations and the analysis is applied along the individual tracks and composited over many TCs. Two methods of compositing are employed: a composite over all TC snapshots in a given intensity range, and a composite over all TC snapshots at the same stage in the TC life cycle (same time relative to the time of lifetime maximum intensity for each storm). The radiative feedback contributes to TC development in all models, especially in storms of weaker intensity or earlier stages of development. Notably, the surface flux feedback is stronger in models that simulate more intense TCs. This indicates that the representation of the interaction between spatially varying surface fluxes and the developing TC is responsible for at least part of the intermodel spread in TC simulation.

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Eric D. Maloney, Andrew Gettelman, Yi Ming, J. David Neelin, Daniel Barrie, Annarita Mariotti, C.-C. Chen, Danielle R. B. Coleman, Yi-Hung Kuo, Bohar Singh, H. Annamalai, Alexis Berg, James F. Booth, Suzana J. Camargo, Aiguo Dai, Alex Gonzalez, Jan Hafner, Xianan Jiang, Xianwen Jing, Daehyun Kim, Arun Kumar, Yumin Moon, Catherine M. Naud, Adam H. Sobel, Kentaroh Suzuki, Fuchang Wang, Junhong Wang, Allison A. Wing, Xiaobiao Xu, and Ming Zhao

Abstract

Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers’ model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.

Open access
Alexis Berg and Justin Sheffield

Abstract

Evapotranspiration (ET) is a key process affecting terrestrial hydroclimate, as it modulates the land surface carbon, energy, and water budgets. Evapotranspiration mainly consists of the sum of three components: plant transpiration, soil evaporation, and canopy interception. Here we investigate how the partitioning of ET into these three main components is represented in CMIP5 model simulations of present and future climate. A large spread exists between models in the simulated mean present-day partitioning; even the ranking of the different components in the global mean differs between models. Differences in the simulation of the vegetation leaf area index appear to be an important cause of this spread. Although ET partitioning is not accurately known globally, existing global estimates suggest that CMIP5 models generally underestimate the relative contribution of transpiration. Differences in ET partitioning lead to differences in climate characteristics over land, such as land–atmosphere fluxes and near-surface air temperature. On the other hand, CMIP5 models simulate robust patterns of future changes in ET partitioning under global warming, notably a marked contrast between decreased transpiration and increased soil evaporation in the tropics, whereas transpiration and evaporation both increase at higher latitudes and both decrease in the dry subtropics. Idealized CMIP5 simulations from a subset of models show that the decrease in transpiration in the tropics largely reflects the stomatal closure effect of increased atmospheric CO2 on plants (despite increased vegetation from CO2 fertilization), whereas changes at higher latitudes are dominated by radiative CO2 effects, with warming and increased precipitation leading to vegetation increase and simultaneous (absolute) increases in all three ET components.

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Douglas E. Miller and Zhuo Wang

Abstract

The representation of ENSO and NAO are examined in the Climate Forecast System, version 2 (CFSv2), reforecasts with a focus on the physical processes related to teleconnections and predictability. CFSv2 predicts ENSO well, but an eastward shift of the tropical Pacific sea surface temperature (SST) anomalies is evident. Although it appears minor on the global scale, the shift in convection and the large-scale wave train affects the model prediction of regional climate. In contrast, NAO is predicted poorly. The anomaly correlation coefficient (ACC) between the model ensemble mean and the observation is 0.27 during 1982–2010, and the ensemble spread is large. The representation of three sources of NAO predictability—SST, the stratospheric polar vortex, and the Arctic sea ice concentration—is investigated. It is found that the link between tropical Pacific SST and NAO is not well represented in CFSv2, and that the tropospheric–stratospheric interactions are too weak, both contributing to the poor prediction of NAO. Additionally, the impact of ENSO and NAO on prediction skill of CFSv2 in boreal winter is analyzed in terms of the spatial ACC of geopotential height. Active ENSO events exhibit larger prediction skill than neutral years, especially during the ENSO+/NAO− and ENSO−/NAO+ winters. Spatial patterns of prediction skill are also examined, and larger skill of geopotential height and 2-m air temperature is found outlined by the nodes of the PNA pattern, consistent with the large signal-to-noise ratios associated with the ENSO teleconnection.

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Grey S. Nearing, Benjamin L. Ruddell, Martyn P. Clark, Bart Nijssen, and Christa Peters-Lidard

Abstract

We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.

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Alexis Berg and Justin Sheffield

Abstract

Soil moisture–atmosphere coupling is a key process underlying climate variability and change over land. The control of soil moisture (SM) on evapotranspiration (ET) is a necessary condition for soil moisture to feed back onto surface climate. Here we investigate how this control manifests itself across simulations from the CMIP5 ensemble, using correlation analysis focusing on the interannual (summertime) time scale. Analysis of CMIP5 historical simulations indicates significant model diversity in SM–ET coupling in terms of patterns and magnitude. We investigate the relationship of this spread with differences in background simulated climate. Mean precipitation is found to be an important driver of model spread in SM–ET coupling but does not explain all of the differences, presumably because of model differences in the treatment of land hydrology. Compared to observations, some land regions appear consistently biased dry and thus likely overly soil moisture–limited. Because of ET feedbacks on air temperature, differences in SM–ET coupling induce model uncertainties across the CMIP5 ensemble in mean surface temperature and variability. We explore the relationships between model uncertainties in SM–ET coupling and climate projections. In particular over mid-to-high-latitude continental regions of the Northern Hemisphere but also in parts of the tropics, models that are more soil moisture–limited in the present tend to warm more in future projections, because they project less increase in ET and (in midlatitudes) greater increase in incoming solar radiation. Soil moisture–atmosphere processes thus contribute to the relationship observed across models between summertime present-day simulated climate and future warming projections over land.

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