scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2011 ◽  
Vol 7 (4) ◽  
pp. 1225-1246 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Local Scale ◽  
Additive Models ◽  
Simple Relationship ◽  
Temperature Conditions

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs (GAM and ML-GAM) is not significantly in better agreement with LGM permafrost data than large-scale fields. At the LGM, both methods do not reduce the variability between climate models results. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with LGM data than in the CTRL period. These differences reduce the contribution of downscaling.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2011 ◽  
Vol 7 (3) ◽  
pp. 1647-1692 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Regional Scale ◽  
Kappa Statistic ◽  
Local Scale ◽  
Additive Models ◽  
Simple Relationship ◽  
Global Agreement

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variation between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then, we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with data. In average for the nine PMIP2 models, we measure a global agreement by kappa statistic of 0.80 with CTRL permafrost data, against 0.68 for the GAM method. In both cases, the provided local information reduces the inter-variation between climate models. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs is not significantly better than large-scale fields with 0.46 (GAM) and 0.49 (ML-GAM) of global agreement with LGM permafrost data. At the LGM, both methods do not reduce the inter-variation between climate models. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with permafrost data, than in the CTRL period. These differences reduce the contribution of downscaling and depend on several other factors deserving further studies.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2010 ◽  
Vol 4 (4) ◽  
pp. 2233-2275 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Multinomial Logistic Regression ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Additive Models ◽  
Simple Relationship ◽  
Time Period

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variability between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of surface air temperature (SAT). Then, we define permafrost distribution over Eurasia by SAT conditions. In a first validation step on present climate (CTRL period), GAM shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a stochastic generator approach through a Multinomial Logistic Regression (MLR), which directly models the probabilities of local permafrost indices. The obtained permafrost distributions appear in a better agreement with data. In both cases, the provided local information reduces the inter-variability between climate models. Nevertheless, this also proves that a simple relationship between permafrost and the SAT only is not always sufficient to represent local permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. Our SDMs do not significantly improve permafrost distribution and do not reduce the inter-variability between climate models, at this period. We show that LGM permafrost distribution from climate models strongly depends on large-scale SAT. The differences with LGM data, larger than in the CTRL period, reduce the contribution of downscaling and depend on several factors deserving further studies.

PREDICTORS AND THEIR DOMAIN FOR STATISTICAL DOWNSCALING OF CLIMATE IN BANGLADESH

2016 ◽  
Vol 78 (6-12) ◽  
Author(s):  
Mahiuddin Alamgir ◽  
Sahar Hadi Pour ◽  
Morteza Mohsenipour ◽  
M. Mehedi Hasan ◽  
Tarmizi Ismail
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Regional Scale ◽  
Circulation Model ◽  
Local Scale ◽  
Mitigation Measures ◽  
Grid Points ◽  
Hydrological Services ◽  
Atmospheric Variables

Reliable projection of future rainfall in Bangladesh is very important for the assessment of possible impacts of climate change and implementation of necessary adaptation and mitigation measures. Statistical downscaling methods are widely used for downscaling coarse resolution general circulation model (GCM) output at local scale. Selection of predictors and their spatial domain is very important to facilitate downscaling future climate projected by GCMs. The present paper reports the finding of the study conducted to identify the GCM predictors and demarcate their climatic domain for statistical downscaling in Bangladesh at local or regional scale. Twenty-six large scale atmospheric variables which are widely simulated GCM predictors from 45 grid points around the country were analysed using various statistical methods for this purpose. The study reveals that large-scale atmospheric variables at the grid points located in the central-west part of Bangladesh have the highest influence on rainfall.  It is expected that the finding of the study will help different meteorological and agricultural organizations of Bangladesh to project rainfall and temperature at local scale in order to provide various agricultural or hydrological services.

Verification of GCM-Generated Regional Seasonal Precipitation for Current Climate and of Statistical Downscaling Estimates under Changing Climate Conditions

1999 ◽  
Vol 12 (1) ◽  
pp. 258-272 ◽  
Author(s):  
Aristita Busuioc ◽  
Hans von Storch ◽  
Reiner Schnur
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Regional Scale ◽  
The Other ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Seasonal Precipitation ◽  
Empirical Downscaling

Abstract Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in “time-slice” mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. The downscaling model is skillful for all seasons except spring. The general features of the large-scale SLP variability are reproduced fairly well by both GCMs in all seasons. The climate models reproduce the empirically determined precipitation–SLP link in winter, whereas the observed link is only partially captured for the other seasons. Thus, these models may be considered skillful with respect to regional precipitation during winter, and partially during the other seasons. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 × CO2 GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.

scholarly journals DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

2021 ◽  
Author(s):  
Dan Lunt ◽  
Keyword(s):  
Boundary Conditions ◽  
Air Temperature ◽  
Ocean Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Model Parameters ◽  
Global Mean

<div> <div> <div>We present results from an ensemble of eight climate models, each of which has carried out simulations of theearly Eocene climate optimum (EECO, ∼50 million years ago). These simulations have been carried out in the framework of DeepMIP (www.deepmip.org), and as such all models have been configured with the same paleogeographic and vegetation boundary conditions. The results indicate that these non-CO<sub>2</sub> boundary conditions contribute between 3 and 5<sup>o</sup>C to Eocene warmth. Compared to results from previous studies, the DeepMIP simulations show in general reduced spread of global mean surface temperature response across the ensemble for a given atmospheric CO<sub>2</sub> concentration, and an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with preindustrial arises mostly from decreases in emissivity due to the elevated CO<sub>2</sub> (and associated water vapour and long-wave cloud feedbacks), whereas in terms of the meridional temperature gradient, the reduction in the Eocene is primarily due to emissivity and albedo changes due to the non-CO<sub>2</sub> boundary conditions (i.e. removal of the Antarctic ice sheet and changes in vegetation). Three of the models (CESM, GFDL, and NorESM) show results that are consistent with the proxies in terms of global mean temperature, meridional SST gradient, and CO<sub>2</sub>, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale the models lack skill. In particular, in the southwest Pacific, the modelled anomalies are substantially less than indicated by the proxies; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxiesor models in this region. Our aim is that the documentation of the large scale features and model-data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability; and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols</div> </div> </div>

Assessing urbanization’s contribution to warming in mainland China using satellite-estimated air temperature data

2021 ◽  
pp. 030913332198885
Author(s):  
Rui Yao ◽  
Lunche Wang ◽  
Xin Huang ◽  
Xiaojun Wu ◽  
Liu Yang ◽  
...  
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Regional Scale ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Local Scale ◽  
Rapid Urbanization ◽  
The Past ◽  
The Mean ◽  
Global Surface

The global surface air temperature (Ta) has increased significantly in the past several decades. However, it remains disputable how much effect rapid urbanization has had on warming trends in mainland China. In this study, a gridded Ta dataset was created using satellite data. Then, a series of satellite-based methods to evaluate the contribution of urbanization to warming were developed. Subsequently, the contribution of urbanization to warming during 2001–2018 was estimated. The national average Ta was found to have increased significantly (0.23°C/decade) in mainland China. At the national scale, the contribution of urbanization to warming was negligible (less than 1%) since built-up areas account for only approximately 2.66% of the area of China. At the regional scale, the contribution of urbanization was also small in most areas and was even negative in some areas. At the local scale, the contributions of urbanization to warming were 53.18%, 54.30% and 47.25% for the mean, maximum and minimum Ta, respectively, averaged for 31 major cities. This study demonstrated that the contribution of urbanization to warming was significant at the local scale, while the contribution of urbanization to large-scale warming was limited. The contribution of urbanization was underestimated at the local scale but overestimated at the national and regional scales by many previous studies due to the sparse and uneven distribution of meteorological stations.

scholarly journals Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe

2007 ◽  
Vol 3 (4) ◽  
pp. 899-933 ◽  
Author(s):  
M. Vrac ◽  
D. Paillard ◽  
P. Naveau
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Western Europe ◽  
Generalized Additive Models ◽  
Circulation Model ◽  
Additive Models ◽  
Future Climate Change ◽  
Small Scale ◽  
Global Circulation Model ◽  
Non Linear

Abstract. The needs of small-scale climate information have become prevalent to study the impacts of future climate change as well as for paleoclimate researches where the reconstructions from proxies are obviously local. In this study we develop a non-linear statistical downscaling method to generate local temperatures and precipitation values from large-scale variables (e.g. Global Circulation Model – GCM – outputs), through Generalized Additive Models (GAMs) calibrated on the present Western Europe climate. First, various monthly GAMs (i.e. one model for each month) are tested for preliminary analysis. Then, annual GAMs (i.e. one model for the 12 months altogether) are developed and tailored for two sets of predictors (geographical and physical) to downscale local temperatures and precipitation. As an evaluation of our approach under large-scale conditions different from present Western Europe, projections are realized (1) for present North America and Northern Europe and compared to local observations (spatial test); and (2) for the Last Glacial Maximum (LGM) period, and compared to local reconstructions and GCMs outputs (temporal test). In general, both spatial and temporal evaluations indicate that the GAMs are flexible and efficient tools to capture and downscale non-linearities between large- and local-scale variables. More precisely, the results emphasize that, while physical predictors alone are not capable of downscaling realistic values when applied to climate strongly different from the one used for calibration, the inclusion of geographical-type variables – such as altitude, advective continentality and W-slope – into GAM predictors brings robustness and improvement to the method and its local projections.

Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

2008 ◽  
Vol 21 (22) ◽  
pp. 6052-6059 ◽  
Author(s):  
B. Timbal ◽  
P. Hope ◽  
S. Charles
Keyword(s):  
Global Warming ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Specific Humidity ◽  
Model Output ◽  
Winter Rainfall ◽  
Direct Model ◽  
The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

scholarly journals Linking trophic cascades to changes in desert dune geomorphology using high-resolution drone data

2018 ◽  
Vol 15 (144) ◽  
pp. 20180327 ◽  
Author(s):  
Mitchell B. Lyons ◽  
Charlotte H. Mills ◽  
Christopher E. Gordon ◽  
Mike Letnic
Keyword(s):  
Vegetation Cover ◽  
Large Scale ◽  
Generalized Additive Models ◽  
Three Dimensional ◽  
Trophic Cascades ◽  
Stable State ◽  
Physical Characteristics ◽  
Additive Models ◽  
Surface Erosion ◽  
Dune Stability

Vegetation cover is fundamental in the formation and maintenance of geomorphological features in dune systems. In arid Australia, increased woody shrub cover has been linked to removal of the apex predator (Dingoes, Canis dingo ) via subsequent trophic cascades. We ask whether this increase in shrubs can be linked to altered physical characteristics of the dunes. We used drone-based remote sensing to measure shrub density and construct three-dimensional models of dune morphology. Dunes had significantly different physical characteristics either side of the ‘dingo-proof fence’, inside which dingoes are systematically eradicated and shrub density is higher over vast spatial extents. Generalized additive models revealed that dunes with increased shrub density were higher, differently shaped and more variable in height profile. We propose that low shrub density induces aeolian and sedimentary processes that result in greater surface erosion and sediment transport, whereas high shrub density promotes dune stability. We speculate that increased vegetation cover acts to push dunes towards an alternate stable state, where climatic variation no longer has a significant effect on their morphodynamic state within the bi-stable state model. Our study provides evidence that anthropogenically induced trophic cascades can indirectly lead to large-scale changes in landscape geomorphology.

scholarly journals A Bootstrap Technique for Testing the Relationship between Local-Scale Radar Observations of Cloud Occurrence and Large-Scale Atmospheric Fields

2006 ◽  
Vol 63 (11) ◽  
pp. 2813-2830 ◽  
Author(s):  
Roger Marchand ◽  
Nathaniel Beagley ◽  
Sandra E. Thompson ◽  
Thomas P. Ackerman ◽  
David M. Schultz
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Global Climate ◽  
Stable State ◽  
Local Scale ◽  
Global Climate Models ◽  
Scale Model ◽  
Synoptic Scale ◽  
Cloud Properties

Abstract A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique. A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.

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