scholarly journals Predictability of phases and magnitudes of natural decadal climate variability phenomena in CMIP5 experiments with the UKMO HadCM3, GFDL-CM2.1, NCAR-CCSM4, and MIROC5 global earth system models

2018 ◽  
Vol 52 (5-6) ◽  
pp. 3255-3275 ◽  
Author(s):  
Vikram M. Mehta ◽  
Katherin Mendoza ◽  
Hui Wang
Keyword(s):  
Climate Variability ◽  
Earth System ◽  
Decadal Climate Variability ◽  
System Models ◽  
Decadal Climate ◽  
Earth System Models

scholarly journals Plant functional type mapping for earth system models

2011 ◽  
Vol 4 (3) ◽  
pp. 2081-2121 ◽  
Author(s):  
B. Poulter ◽  
P. Ciais ◽  
E. Hodson ◽  
H. Lischke ◽  
F. Maignan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Climate Variability ◽  
Forest Cover ◽  
Plant Functional Type ◽  
Earth System ◽  
Functional Type ◽  
System Models ◽  
Satellite Sensors ◽  
Earth System Models

Abstract. The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistry-climate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into just several classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Köppen-Geiger climate zones. Using a beta (β) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeography-biogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30 % (20 %) uncertainty in the sensitivity of GPP (transpiration) to precipitation. The availability of plant functional type datasets that are consistent with current satellite products and adapted for earth system models is an important component for reducing the uncertainty of terrestrial biogeochemistry to climate variability.

From the last interglacial to the future – new insights from modeling the last glacial-interglacial cycle in PalMod

2021 ◽  
Author(s):  
Kerstin Fieg ◽  
Mojib Latif ◽  
Michael Schulz ◽  
Tatjana Ilyina
Keyword(s):  
Climate Variability ◽  
Ice Sheet ◽  
Earth System ◽  
Glacial Cycle ◽  
System Models ◽  
Last Glacial ◽  
The Future ◽  
Fully Coupled ◽  
Earth System Models ◽  
The Last Glacial

<p>We present new insights from the project PalMod, which started in 2016 and is envisioned to run for a decade. The modelling initiative PalMod aims at filling the long-standing scientific gaps in our understanding of the dynamics and variability of the climate system during the last glacial-interglacial cycle. One of the grand challenges in this context is to quantify the processes that determine the spectrum of climate variability on timescales that range from seasons to millennia. Climatic processes are intimately coupled across these timescales. Understanding variability at any one timescale requires understanding of the whole spectrum. If we could successfully simulate the spectrum of climate variability during the last glacial cycle in Earth system models, would this enable us to more reliably assess the future climate change? Such simulations are necessary to deduce, for example, if a regime shift in climate variability could occur during the next centuries and millennia in response to global warming. PalMod is specifically designed to enhance our understanding of the Earth system dynamics and its variability on timescales up to the multimillennial with complex Earth System Models.</p><p>The following major goals were achieved up to now:</p><ul><li>Full coupling of atmosphere, ocean and ice-sheet models, enabling investigation of Heinrich Events and bi-stability of the AMOC, and millennial-scale transient climate-ice sheet simulations.</li> <li>Implementation of a coupled ocean and land biogeochemistry enabling simulations with prognostic atmospheric CO<sub>2</sub> concentrations and including improved representation of methane (CH<sub>4</sub>) in transient deglaciation runs.</li> <li>Systematic comparison of newly compiled proxy data with model simulations.</li> </ul><p>The major goal for the next two years is to set up the fully coupled physical-biogeochemical model which will be tested for three time periods: deglaciation, glacial inception and Marine Isotope Stage 3 (MIS3). This fully coupled model will be eventually used to simulate the complete glacial cycle and project the climate over the next few millennia.</p>

scholarly journals Plant functional type mapping for earth system models

2011 ◽  
Vol 4 (4) ◽  
pp. 993-1010 ◽  
Author(s):  
B. Poulter ◽  
P. Ciais ◽  
E. Hodson ◽  
H. Lischke ◽  
F. Maignan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Climate Variability ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Earth System ◽  
State Variables ◽  
System Models ◽  
Satellite Sensors ◽  
Earth System Models

Abstract. The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistry-climate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into usually only 10–20 classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Köppen-Geiger climate zones. Using a beta (ß) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeography-biogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30% (20%) uncertainty in the sensitivity of GPP (transpiration) to precipitation. The availability of PFT datasets that are consistent with current satellite products and adapted for earth system models is an important component for reducing the uncertainty of terrestrial biogeochemistry to climate variability.

scholarly journals Resolving and Parameterising the Ocean Mesoscale in Earth System Models

2020 ◽  
Vol 6 (4) ◽  
pp. 137-152
Author(s):  
Helene T. Hewitt ◽  
Malcolm Roberts ◽  
Pierre Mathiot ◽  
Arne Biastoch ◽  
Ed Blockley ◽  
...  
Keyword(s):  
Climate Variability ◽  
Meridional Overturning Circulation ◽  
Earth System ◽  
System Models ◽  
Recent Developments ◽  
Meridional Overturning ◽  
The Mean ◽  
The Impact ◽  
Earth System Models ◽  
Mean State

Abstract Purpose of Review Assessment of the impact of ocean resolution in Earth System models on the mean state, variability, and future projections and discussion of prospects for improved parameterisations to represent the ocean mesoscale. Recent Findings The majority of centres participating in CMIP6 employ ocean components with resolutions of about 1 degree in their full Earth System models (eddy-parameterising models). In contrast, there are also models submitted to CMIP6 (both DECK and HighResMIP) that employ ocean components of approximately 1/4 degree and 1/10 degree (eddy-present and eddy-rich models). Evidence to date suggests that whether the ocean mesoscale is explicitly represented or parameterised affects not only the mean state of the ocean but also the climate variability and the future climate response, particularly in terms of the Atlantic meridional overturning circulation (AMOC) and the Southern Ocean. Recent developments in scale-aware parameterisations of the mesoscale are being developed and will be included in future Earth System models. Summary Although the choice of ocean resolution in Earth System models will always be limited by computational considerations, for the foreseeable future, this choice is likely to affect projections of climate variability and change as well as other aspects of the Earth System. Future Earth System models will be able to choose increased ocean resolution and/or improved parameterisation of processes to capture physical processes with greater fidelity.

scholarly journals The evaluation of Earth System Models: discussion summary

2011 ◽  
Vol 6 ◽  
pp. 216-221
Author(s):  
Sönke Zaehle ◽  
Colin Prentice ◽  
Sarah Cornell
Keyword(s):  
Earth System ◽  
System Models ◽  
Earth System Models

scholarly journals Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

2015 ◽  
Vol 8 (4) ◽  
pp. 3235-3292 ◽  
Author(s):  
A. L. Atchley ◽  
S. L. Painter ◽  
D. R. Harp ◽  
E. T. Coon ◽  
C. J. Wilson ◽  
...  
Keyword(s):  
Carbon Sink ◽  
Thermal Response ◽  
Field Measurements ◽  
Early Summer ◽  
Active Layer Thickness ◽  
Earth System ◽  
Model Refinement ◽  
System Models ◽  
Developed Surface ◽  
Earth System Models

Abstract. Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth System Models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth System Models challenge validation and parameterization of hydrothermal models. A recently developed surface/subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to calibrate and identify fine scale controls of ALT in ice wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze/thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g. troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.

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