Underlying causes of Eurasian midcontinental aridity in simulations of mid‐Holocene climate

Bartlein, Patrick J.; Harrison, Sandy P.; Izumi, Kenji

doi:10.1002/2017gl074476

Cited by 30 publications

(29 citation statements)

References 120 publications

(132 reference statements)

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“…These changes gave rise to a climate radically different from that of today; indeed the magnitude of the change in radiative forcing between LGM and pre-industrial climate is comparable to highemissions projections of climate change between now and the end of the 21st century (Braconnot et al, 2012). The LGM has been a focus for model evaluation in the Paleoclimate Modelling Intercomparison Project (PMIP) since its inception (Joussaume and Taylor, 1995;Braconnot et al, 2007Braconnot et al, , 2012. The LGM is one of the two "entry card" palaeoclimate simulations included in the current phase of the Coupled Model Intercomparison Project (CMIP6) (Kageyama et al, 2018).…”

Section: S F Cleator Et Al: Multivariable Lgm Palaeoclimate Reconsmentioning

confidence: 99%

A new multivariable benchmark for Last Glacial Maximum climate simulations

et al. 2020

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Abstract. We present a new global reconstruction of seasonal climates at the Last Glacial Maximum (LGM, 21 000 years BP) made using 3-D variational data assimilation with pollen-based site reconstructions of six climate variables and the ensemble average of the PMIP3—CMIP5 simulations as a prior (initial estimate of LGM climate). We assume that the correlation matrix of the uncertainties in the prior is both spatially and temporally Gaussian, in order to produce a climate reconstruction that is smoothed both from month to month and from grid cell to grid cell. The pollen-based reconstructions include mean annual temperature (MAT), mean temperature of the coldest month (MTCO), mean temperature of the warmest month (MTWA), growing season warmth as measured by growing degree days above a baseline of 5 ∘C (GDD5), mean annual precipitation (MAP), and a moisture index (MI), which is the ratio of MAP to mean annual potential evapotranspiration. Different variables are reconstructed at different sites, but our approach both preserves seasonal relationships and allows a more complete set of seasonal climate variables to be derived at each location. We further account for the ecophysiological effects of low atmospheric carbon dioxide concentration on vegetation in making reconstructions of MAP and MI. This adjustment results in the reconstruction of wetter climates than might otherwise be inferred from the vegetation composition. Finally, by comparing the uncertainty contribution to the final reconstruction, we provide confidence intervals on these reconstructions and delimit geographical regions for which the palaeodata provide no information to constrain the climate reconstructions. The new reconstructions will provide a benchmark created using clear and defined mathematical procedures that can be used for evaluation of the PMIP4–CMIP6 entry-card LGM simulations and are available at https://doi.org/10.17864/1947.244 (Cleator et al., 2020b).

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Section: S F Cleator Et Al: Multivariable Lgm Palaeoclimate Reconsmentioning

confidence: 99%

A new multivariable benchmark for Last Glacial Maximum climate simulations

et al. 2020

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“…Climate and climate variability are determined by the amount of incoming solar radiation, chemical composition and dynamics of the atmosphere, and surface characteristics of the earth [1][2][3]. The surface characteristics of the earth often affect the regional and local patterns of climate through the redistribution of energy [4][5][6]. As a unique underlying surface covering about 40% of terrestrial surface, deserts make a vital contribution to the global climate system [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Warm Island Effect in the Badain Jaran Desert Lake Group Region Inferred from the Accumulated Temperature

Zhao

Niu

et al. 2020

Atmosphere

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The Badain Jaran Desert (BJD) is characterized by the coexistence of over 110 perennial lakes and thousands of megadunes in its southeast part. Unlike the cold island effect, we found a special phenomenon of the warm island effect in the lake group region of the BJD. However, the concept and formation mechanism remains unclear. In this study, based on observations of land surface processes in the area, we first used the daily mean temperature from 23 automated meteorological stations from 2010 to 2017 to calculate the mean daily temperature (T) ≥ 0 °C, T ≥ 10 °C accumulated temperature and negative accumulated temperature. Furthermore, using the net radiation from two eddy covariance measurement systems, characteristics of the net radiation between the lake and megadunes were analyzed. When comparing observed data in the lake group region to surrounding areas, accumulated temperature from all three meteorological stations in the lake group region were higher; the duration days of T ≥ 0 °C and T ≥ 10 °C were longer, whereas duration days of negative accumulated temperature were shorter. In addition, the initial dates for T ≥ 0 °C and T ≥ 10 °C accumulated temperature were earlier, whereas the end dates were delayed. Variations in heat were observed between stations in the lake group region that may be reflective of microclimate environments between lakes. The authors relate warm island formation in the BJD lake group region to (1) the heat carried by groundwater recharge to the desert lake groups has a great impact on the local temperature. (2) Net heat radiation to the atmosphere through sensible heat flux owing to sparse vegetation in the desert areas. Hence, heat resources are richer in the lake group region. This study aims to improve our understanding of the warm island effect from a comprehensive analysis of its intensity and distribution pattern around the lake group region as compared to its surroundings. In addition, the results from this study will provide a scientific basis for determining the source of lake water in the BJD.

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“…The evaluation of previous generations of palaeoclimate simulations has shown that the large-scale thermodynamic responses seen in 21 st century and LGM climates, including enhanced land-sea temperature contrast, latitudinal amplification, and scaling of precipitation with temperature, are likely to be realistic (Izumi et al, 2013;Li et al, 2013;Lunt et al, 2013;Hill et al, 2014;Izumi et al, 2014;Harrison et al, 2015). However, evaluation against palaeodata shows that even when the sign of large-scale climate changes is correctly predicted, the patterns of change at a regional scale are often inaccurate and the magnitudes of change often underestimated (Brewer et al, 2007;Mauri et al, 2014;Perez Sanz et al, 2014;Bartlein et al, 2017). The current focus on understanding what causes mismatches between reconstructed and simulated climates is a primary motivation for developing benchmark data sets that represent regional climate changes comprehensively enough to allow a critical evaluation of model deficiencies.…”

Section: Introductionmentioning

confidence: 99%

A new multi-variable benchmark for Last Glacial Maximum climate simulations

Cleator¹,

Harrison²,

K³

et al. 2019

Preprint

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Abstract. We present a new global reconstruction of seasonal climates at the Last Glacial Maximum (LGM, 21,000 yr BP) made using 3-D variational data assimilation with pollen-based site reconstructions of six climate variables and the ensemble average of the PMIP3/CMIP5 simulations as a prior. We assume that the correlation matrix of the errors of the prior both spatially and temporally is Gaussian, in order to produce a climate reconstruction that is smoothed both from month to month and from grid cell to grid cell. The pollen-based reconstructions include mean annual temperature (MAT), mean temperature of the coldest month (MTCO), mean temperature of the warmest month (MTWA), growing season warmth as measured by growing degree days above a baseline of 5 °C (GDD5), mean annual precipitation (MAP) and a moisture index (MI), which is the ratio of MAP to mean annual potential evapotranspiration. Different variables are reconstructed at different sites, but our approach both preserves seasonal relationships and allows a more complete set of seasonal climate variables to be derived at each location. We further account for the ecophysiological effects of low atmospheric carbon dioxide concentration on vegetation in making reconstructions of MAP and MI. This adjustment results in the reconstruction of wetter climates than might otherwise be inferred by the vegetation composition. Finally, by comparing the error contribution to the final reconstruction, we provide confidence intervals on these reconstructions and delimit geographical regions for which the palaeodata provide no information to constrain the climate reconstructions. The new reconstructions will provide a robust benchmark for evaluation of the PMIP4/CMIP6 entry-card LGM simulations.

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Underlying causes of Eurasian midcontinental aridity in simulations of mid‐Holocene climate

Cited by 30 publications

References 120 publications

A new multivariable benchmark for Last Glacial Maximum climate simulations

A new multivariable benchmark for Last Glacial Maximum climate simulations

Warm Island Effect in the Badain Jaran Desert Lake Group Region Inferred from the Accumulated Temperature

A new multi-variable benchmark for Last Glacial Maximum climate simulations

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