This work examines different conceptions of land surface model benchmarking and the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and ...model resolutions. It additionally demonstrates how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and gives an example of how these expectations might be quantified. Finally, the Protocol for the Analysis of Land Surface models (PALS) is introduced - a free, online land surface model benchmarking application that is structured to meet both of these goals.
Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by ...collating model estimates from different research institutions internationally with the assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a “perfect model” approach is used to test whether a previously proposed ensemble dependence transformation (EDT) can improve twenty-first-century Coupled Model Intercomparison Project (CMIP) projections. In these tests, where twenty-first-century model simulations are used as out-of-sample “observations,” the mean-square difference between the transformed ensemble mean and “observations” is on average 30% less than for the untransformed ensemble mean. In addition, the variance of the transformed ensemble matches the variance of the ensemble mean about the “observations” much better than in the untransformed ensemble. Results show that the EDT has a significant effect on twenty-first-century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16% (0.2°C for B1 scenario), regional average temperatures by as much as 2.6°C (RCP8.5 scenario), and regional average annual rainfall by as much as 410 mm (RCP6.0 scenario). In some regions, however, the effect is minimal. It is also found that the EDT causes changes to temperature projections that differ in sign for different emissions scenarios. This may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Plumbing of Land Surface Models Best, M. J.; Abramowitz, G.; Johnson, H. R. ...
Journal of hydrometeorology,
06/2015, Letnik:
16, Številka:
3
Journal Article
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The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. ...Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically basedmodels and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Land surface models (LSMs) must accurately simulate observed energy and water fluxes during droughts in order to provide reliable estimates of future water resources. We evaluated 8 different LSMs ...(14 model versions) for simulating evapotranspiration (ET) during periods of evaporative drought (Edrought) across six flux tower sites. Using an empirically defined Edrought threshold (a decline in ET below the observed 15th percentile), we show that LSMs simulated 58 Edrought days per year, on average, across the six sites, ∼3 times as many as the observed 20 d. The simulated Edrought magnitude was ∼8 times greater than observed and twice as intense. Our findings point to systematic biases across LSMs when simulating water and energy fluxes under water-stressed conditions. The overestimation of key Edrought characteristics undermines our confidence in the models' capability in simulating realistic drought responses to climate change and has wider implications for phenomena sensitive to soil moisture, including heat waves.
Stomatal conductance (gs) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model ...within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between gs model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We constrained the key model parameter "g1", which represents plant water use strategy, by PFT, based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the g1 parameter can be estimated using two long-term average (1960–1990) bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal model, in conjunction with PFT parameterisations, resulted in a large reduction in annual fluxes of transpiration (~ 30% compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance against upscaled data products was not degraded, but did not noticeably reduce existing model–data biases. We identified assumptions relating to the coupling of the vegetation to the atmosphere and the parameterisation of the minimum stomatal conductance as areas requiring further investigation in both CABLE and potentially other LSMs. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.
Global climate models play an important role in quantifying past and projecting future changes in drought. Previous studies have pointed to shortcomings in these models for simulating droughts, but ...systematic evaluation of their level of agreement has been limited. Here, historical simulations (1950–2004) for 20 models from the latest Coupled Model Intercomparison Project (CMIP5) were analyzed for a variety of drought metrics and thresholds using a standardized drought index. Model agreement was investigated for different types of drought (precipitation, runoff, and soil moisture) and how this varied with drought severity and duration. At the global scale, climate models were shown to agree well on most precipitation drought metrics, but systematically underestimated precipitation drought intensity compared to observations. Conversely, simulated runoff and soil moisture droughts varied significantly across models, particularly for intensity. Differences in precipitation simulations were found to explain model differences in runoff and soil moisture drought metrics over some regions, but predominantly with respect to drought intensity. This suggests it is insufficient to evaluate models for precipitation droughts to increase confidence in model performance for other types of drought. This study shows large but metric-dependent discrepancies in CMIP5 for modeling different types of droughts that relate strongly to the component models (i.e., atmospheric or land surface scheme) used in the coupled modeling systems. Our results point to a need to consider multiple models in drought impact studies to account for high model uncertainties.
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Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
A climate model, coupled to a sophisticated land surface scheme, is used to explore the impact of land use induced land cover change (LULCC) on climate extremes indices recommended by the Expert Team ...on Climate Change Detection and Indices (ETCCDI). The impact from LULCC is contrasted with the impact of doubling atmospheric carbon dioxide (CO2). Many of the extremes indices related to temperature are affected by LULCC and the resulting changes are locally and field significant. Some indices are systematically affected by LULCC in the same direction as increasing CO2 while for others LULCC opposes the impact of increasing CO2. We suggest that assumptions that anthropogenically induced changes in temperature extremes can be approximated just by increasing greenhouse gases are flawed, as LULCC may regionally mask or amplify the impact of increasing CO2 on climate extremes. In some regions, the scale of the LULCC forcing is of a magnitude similar to the impact of CO2 alone. We conclude that our results complicate detection and attribution studies, but also offer a way forward to a clearer and an even more robust attribution of the impact of increasing CO2 at regional scales.
Key Points
Temperature extremes indices are affected by LULCC
LULCC may locally mask or amplify impact of increasing CO2 on extreme indices
Results offer way towards clearer, more robust attribution of CO2 impact
Reliable projections of future climate require land–atmosphere carbon (C) fluxes to be represented realistically in Earth system models (ESMs). There are several sources of uncertainty in how carbon ...is parameterised in these models. First, while interactions between the C, nitrogen (N) and phosphorus (P) cycles have been implemented in some models, these lead to diverse changes in land–atmosphere fluxes. Second, while the first-order parameterisation of soil organic matter decomposition is similar between models, formulations of the control of the soil physical state on microbial activity vary widely. For the first time, we address these sources of uncertainty simultaneously by implementing three soil moisture and three soil temperature respiration functions in an ESM that can be run with three degrees of biogeochemical nutrient limitation (C-only, C and N, and C and N and P). All 27 possible combinations of response functions and biogeochemical mode are equilibrated before transient historical (1850–2005) simulations are performed. As expected, implementing N and P limitation reduces the land carbon sink, transforming some regional sinks into net sources over the historical period. Meanwhile, regardless of which nutrient mode is used, various combinations of response functions imply a two-fold difference in the net ecosystem accumulation and a four-fold difference in equilibrated total soil C. We further show that regions with initially larger pools are more likely to become carbon sources, especially when nutrient availability limits the response of primary production to increasing atmospheric CO2. Simulating changes in soil C content therefore critically depends on both nutrient limitation and the choice of respiration functions.
Accurate projections of climate change and associated extreme events under differing emission scenarios are linked to realistic representations of the temporal variability of the atmosphere at a ...variety of time scales, for example, annual, seasonal, synoptic, and daily. Here a new method is employed to explicitly quantify a model's ability to accurately represent covariance at and between differing time scales. From our global‐scale analysis, on average, raw Coupled Model Intercomparison Project Phase 6 (CMIP6) models misrepresent temporal variances at differing time scales for maximum temperature (tasmax) by a considerable margin, particularly at 183‐, 92‐ and 46‐day time scales. To ameliorate such variability errors, we propose a novel Time Variability Correction (TVC) method that corrects temporal covariances while preserving the essential time‐event sequence of the model simulations. We adopt a model‐as‐truth framework to evaluate the effectiveness of the TVC method under future forcing conditions when applied to daily tasmax simulations from 23 CMIP6 models for 1% of the global grid cells. TVC‐corrected temperatures generally show improved matching of temporal variance and lag correlations in the out‐of‐sample projection period compared to simple mean‐corrected projections. By imparting more realistic temporal‐correlations to model series, TVC is expected to improve the projections of extreme events associated with persistent heat, such as heatwaves. Applying TVC to future temperature projections using actual observations significantly increases the temperature variance in most middle to high latitude land regions in the Northern Hemisphere, while decreasing it in most low to middle latitude land regions, compared to simple mean‐corrected projections.
Plain Language Summary
Climate change projections are often used to estimate future weather and climate conditions under differing emission scenarios. Accurate projections are required to usefully inform decision making and long‐term planning. In this work, we propose a novel statistical technique to (a) quantify time series variance errors at a range of time scales, such as annual, seasonal, synoptic and so on; (b) correct these errors. By implementing the first step, we find that the time variability of the daily maximum temperature is poorly represented across a range of time scales in raw Coupled Model Intercomparison Project Phase 6 (CMIP6) models. These variability errors are then corrected in the second step. After applying the new technique to the global cases, we demonstrate that it is a powerful tool to improve variance and time‐lag correlation attributes of model simulations.
Key Points
Time series variability within and across time scales is quantified based on backward looking time averages of differing lengths
Coupled Model Intercomparison Project Phase 6 models grossly misrepresent temporal variances of maximum temperatures at differing time scales, particularly seasonal time scales
This new Time Variability Correction method significantly improves the temporal variability and autocorrelation of model time series
One of the most challenging questions regarding the nature and neural basis of consciousness is the embodied dimension of the phenomenon, that is, feeling located within the body and viewing the ...world from that spatial perspective. Current theories in neurophysiology highlight the active role of multisensory and sensorimotor integration in supporting self-location and self-perspective, and propose the right temporal-parietal-junction (rTPJ) as a key area for such function. These theories are based mainly on findings from two experimental paradigms: manipulation of bottom-up multisensory information integration regarding one's body location (full-body illusion), or direct and invasive manipulation disrupting brain activity at the rTPJ. In this study we take a different approach by using hypnotic suggestion – a non-invasive top-down technique – to manipulate the subjective experience of self-location. The brain activity of 18 right-handed participants was recorded using magnetoencephalography (MEG) while their subjective experience of self-location was hypnotically manipulated. Spectral analyses were conducted on the spontaneous MEG data before and during an induction of an out-of-body experience (OBE) by a trained psychiatrist. The results indicate high correlations between power at alpha and high-gamma frequency-bands and the degree of perceived change in self-location. Regions exhibiting such correlations include temporal-occipital regions, the rTPJ, as well as frontal and midline regions. These findings are in line with an oscillatory-based predictive coding framework.
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