Detecting hotspots of atmosphere–vegetation interaction via slowing down – Part 2: Application to a global climate model

Abstract. Early warning signals (EWS) have become a popular statistical tool to infer stability properties of the climate system. In Part 1 of this two-part paper we have presented a diagnostic method to find the hotspot of a sudden transition as opposed to regions that experience an externally induced tipping as a mere response. Here, we apply our method to the atmosphere–vegetation model PlanetSimulator (PlaSim) – VECODE using a regression model. For each of two vegetation collapses in PlaSim-VECODE, we identify a hotspot of one particular grid cell. We demonstrate with additional experiments that the detected hotspots are indeed a particularly sensitive region in the model and give a physical explanation for these results. The method can thus provide information on the causality of sudden transitions and may help to improve the knowledge on the vulnerability of certain subsystems in climate models.

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Detecting hotspots of atmosphere-vegetation interaction via slowing down – Part 2: Application to a global climate model

Earth System Dynamics Discussions ◽

10.5194/esdd-3-683-2012 ◽

2012 ◽

Vol 3 (2) ◽

pp. 683-713

Author(s):

S. Bathiany ◽

M. Claussen ◽

K. Fraedrich

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Grid Cell ◽

Physical Explanation ◽

Vegetation Model ◽

Statistical Tool ◽

Slowing Down ◽

Sudden Transition

Abstract. Early Warning Signals (EWS) have become a popular statistical tool to infer stability properties of the climate system. In Part 1 of this two-part paper we have presented a diagnostic method to find the hotspot of a sudden transition as opposed to regions that experience an externally induced tipping as a mere response. Here, we apply our method to the atmosphere-vegetation model PlanetSimulator (PlaSim) – VECODE using a surrogate stochastic toy model. For each of two vegetation collapses in PlaSim-VECODE we find a hotspot of one particular grid cell. We demonstrate with additional experiments that the detected hotspots are indeed a particularly sensitive region in the model and give a physical explanation for these results. The method can thus provide information on the causality of sudden transitions and may help to improve the knowledge on the vulnerability of certain subsystems in climate models.

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The MJO-QBO Relationship in a GCM with Stratospheric Nudging

Journal of Climate ◽

10.1175/jcli-d-20-0636.1 ◽

2021 ◽

pp. 1-69

Author(s):

Zane Martin ◽

Clara Orbe ◽

Shuguang Wang ◽

Adam Sobel

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Global Climate Models ◽

Boreal Winter ◽

Quasi Biennial Oscillation ◽

Meridional Winds ◽

Goddard Institute ◽

Minimal Bias

AbstractObservational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

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Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

The Journal of Agricultural Science ◽

10.1017/s0021859616000654 ◽

2016 ◽

Vol 155 (3) ◽

pp. 407-420 ◽

Cited By ~ 7

Author(s):

R. S. SILVA ◽

L. KUMAR ◽

F. SHABANI ◽

M. C. PICANÇO

Keyword(s):

Climate Change ◽

Global Warming ◽

Open Field ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Current Model ◽

Global Climate Models ◽

The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

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Impact of heterogeneous chemistry on the distribution of Glyoxal and Methylglyoxal in the troposphere

10.5194/egusphere-egu21-10780 ◽

2021 ◽

Author(s):

Ramiro Checa-Garcia ◽

Didier Didier Hauglustaine ◽

Yves Balkanski ◽

Paola Formenti

Keyword(s):

Atmospheric Chemistry ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Experimental Simulation ◽

Heterogeneous Chemistry ◽

Closed Set ◽

Sensitivity Studies ◽

The Tropics

Glyoxal (GL) and methylglyoxal (MGL) are the smallest di-carbonyls present in the atmosphere. They hydrate easily, a process that is followed by an oligomerisation. As a consequence, it is considered that they participate actively in the formation of secondary organic aerosols (SOA) and therefore, they are being introduced in the current climate models with interactive chemistry to assess their importance on atmospheric chemistry. In our study we present the introduction of glyoxal in the INCA global model. A new closed set of gas-phase&#160; reactions is analysed first with a box model. Then the simulated global distribution of glyoxal by the global climate model is compared with satellite observations. We show that the oxidation of volatile organic compounds and acetylene, together with the photolysis of more complex di-carbonyls allows us to reproduce well glyoxal seasonal cycle in the tropics but it requires an additional sink in several northern hemispheric regions. Additional sensitivity studies are being conducted by introducing &#160;GL and MGL interactions with dust and SOA according to new uptake &#160;coefficients obtained by dedicated experiments in the CESAM instrument (Chamber of Experimental Simulation of Atmospheric Multiphases). The effects of these heterogeneous chemistry processes will be quantified in the light of the new chamber measurements &#160;and also evaluated in terms of optical properties of aged dust aerosol &#160;and the changes in direct radiative effects &#160;of the involved aerosol species.

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How detailed do cloud microphysics need to be in climate models?

10.5194/egusphere-egu21-7115 ◽

2021 ◽

Author(s):

Ulrike Proske ◽

Sylvaine Ferrachat ◽

David Neubauer ◽

Ulrike Lohmann

Keyword(s):

Radiative Forcing ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Computing Time ◽

Cloud Microphysics ◽

Model Response ◽

Microphysical Processes ◽

Improved Accuracy

Clouds are of major importance for the climate system, but the radiative forcing resulting from their interaction with aerosols remains uncertain. To improve the representation of clouds in climate models, the parameterisations of cloud microphysical processes (CMPs) have become increasingly detailed. However, more detailed climate models do not necessarily result in improved accuracy for estimates of radiative forcing (Knutti and Sedl&#225;&#269;ek, 2013; Carslaw et al., 2018). On the contrary, simpler formulations are cheaper, sufficient for some applications, and allow for an easier understanding of the respective process' effect in the model.This study aims to gain an understanding which CMP parameterisation complexity is sufficient through simplification. We gradually phase out processes such as riming or aggregation from the global climate model ECHAM-HAM, meaning that the processes are only allowed to exhibit a fraction of their effect on the model state. The shape of the model response as a function of the artificially scaled effect of a given process helps to understand the importance of this process for the model response and its potential for simplification. For example, if partially removing a process induces only minor alterations in the present day climate, this process presents as a good candidate for simplification. This may be then further investigated, for example in terms of computing time. The resulting sensitivities to CMP complexity are envisioned to guide CMP model simplifications as well as steer research towards those processes where a more accurate representation proves to be necessary.&#160; Carslaw, Kenneth, Lindsay Lee, Leighton Regayre, and Jill Johnson (Feb. 2018). &#8220;Climate Models Are Uncertain, but We Can Do Something About It&#8221;. In: Eos 99. doi: 10.1029/2018EO093757Knutti, Reto and Jan Sedl&#225;&#269;ek (Apr. 2013). &#8220;Robustness and Uncertainties in the New CMIP5 Climate Model Projections&#8221;. In: Nature Climate Change 3.4, pp. 369&#8211;373. doi: 10.1038/nclimate1716

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Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

Journal of Climate ◽

10.1175/jcli-d-18-0104.1 ◽

2018 ◽

Vol 32 (1) ◽

pp. 195-212 ◽

Cited By ~ 9

Author(s):

Sicheng He ◽

Jing Yang ◽

Qing Bao ◽

Lei Wang ◽

Bin Wang

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Yangtze River Basin ◽

Global Climate Model ◽

Global Climate Models ◽

Global Ocean ◽

East China ◽

Cmip5 Models

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

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Dust Aerosol Important for Snowball Earth Deglaciation

Journal of Climate ◽

10.1175/2010jcli3378.1 ◽

2010 ◽

Vol 23 (15) ◽

pp. 4121-4132 ◽

Cited By ~ 26

Author(s):

Dorian S. Abbot ◽

Itay Halevy

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Atmospheric Model ◽

Dust Aerosol ◽

Snowball Earth ◽

Dust Aerosols ◽

Co2 Levels ◽

The Impact

Abstract Most previous global climate model simulations could only produce the termination of Snowball Earth episodes at CO2 partial pressures of several tenths of a bar, which is roughly an order of magnitude higher than recent estimates of CO2 levels during and shortly after Snowball events. These simulations have neglected the impact of dust aerosols on radiative transfer, which is an assumption of potentially grave importance. In this paper it is argued, using the Dust Entrainment and Deposition (DEAD) box model driven by GCM results, that atmospheric dust aerosol concentrations may have been one to two orders of magnitude higher during a Snowball Earth event than today. It is furthermore asserted on the basis of calculations using NCAR’s Single Column Atmospheric Model (SCAM)—a radiative–convective model with sophisticated aerosol, cloud, and radiative parameterizations—that when the surface albedo is high, such increases in dust aerosol loading can produce several times more surface warming than an increase in the partial pressure of CO2 from 10−4 to 10−1 bar. Therefore the conclusion is reached that including dust aerosols in simulations may reconcile the CO2 levels required for Snowball termination in climate models with observations.

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Black Carbon Increases Frequency of Extreme ENSO Events

Journal of Climate ◽

10.1175/jcli-d-19-0549.1 ◽

2019 ◽

Vol 32 (23) ◽

pp. 8323-8333 ◽

Cited By ~ 3

Author(s):

Sijia Lou ◽

Yang Yang ◽

Hailong Wang ◽

Jian Lu ◽

Steven J. Smith ◽

...

Keyword(s):

Black Carbon ◽

Climate Models ◽

Climate Model ◽

Southern Oscillation ◽

Global Climate ◽

Global Climate Model ◽

Co2 Concentration ◽

Enso Events ◽

Aerosol Forcing ◽

Earth’S Climate

ABSTRACT El Niño–Southern Oscillation (ENSO) is the leading mode of Earth’s climate variability at interannual time scales with profound ecological and societal impacts, and it is projected to intensify in many climate models as the climate warms under the forcing of increasing CO2 concentration. Since the preindustrial era, black carbon (BC) emissions have substantially increased in the Northern Hemisphere. But how BC aerosol forcing may influence the occurrence of the extreme ENSO events has rarely been investigated. In this study, using simulations of a global climate model, we show that increases in BC emissions from both the midlatitudes and Arctic weaken latitudinal temperature gradients and northward heat transport, decrease tropical energy divergence, and increase sea surface temperature over the tropical oceans, with a surprising consequential increase in the frequency of extreme ENSO events. A corollary of this study is that reducing BC emissions might serve to mitigate the possible increasing frequency of extreme ENSO events under greenhouse warming, if the modeling result can be translated into the climate in reality.

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Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽

10.3390/ecws-3-05819 ◽

2018 ◽

Vol 7 (1) ◽

pp. 23 ◽

Cited By ~ 1

Author(s):

Carlos Garijo ◽

Luis Mediero

Keyword(s):

Climate Change ◽

Iberian Peninsula ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Control Period ◽

Regional Climate Models ◽

The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

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Quantification of terrestrial regional climate variability from a large database of pollen-based reconstructions and implications for future projections

10.5194/egusphere-egu2020-1203 ◽

2020 ◽

Author(s):

Raphael Hébert ◽

Ulrike herzschuh ◽

Thomas Laepple

Keyword(s):

Climate Variability ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Irregular Sampling ◽

Large Database ◽

Spatial Correlation Structure ◽

Pollen Records

Multidecadal to millenial timescale climate variability has been investigated over the oceanusing extensive proxy data and it was found to yield coherent interproxy estimates of global and regional sea-surface temperature (SST) climate variability (Laepple and Huybers, 2014). Global Climate Model (GCM) simulations on the other hand, were found to exhibit an increasingly large deficit of regional SST climate variability for increasingly longer timescales.Further investigation is needed to better quantify terrestrial climate variability for longtimescales and validate climate models.Vegetation related proxies such as tree rings and pollen records are the most widespreadtypes of archives available to investigate terrestrial climate variability. Tree ring records areparticularly useful for short time scales estimates due to their annual resolution, while pollen-based reconstructions are necessary to cover the longer timescales. In the present work, we use a large database of 1873 pollen records covering the northern hemisphere in order to quantify Holocene vegetation and climate variability for the first time at centennial to multi-millenial timescales.To ensure the robustness of our results, we are particularly interested in the spatio-temporal representativity of the archived signal in pollen records after taking into account the effective spatial scale, the intermittent and irregular sampling, the age-uncertainty and the sediment mixing effect. A careful treatment of the proxy formation allows us to investigate the spatial correlation structure of the pollen-based climate reconstructions as a function of timescales. The pollen data results are then contrasted with the analysis replicated using transient Holocene simulations produced with state-of-the-art climate models as well as stochastic climate model simulations.Our results indicate a substantial gap in terrestrial climate variability between the climate model simulations and the pollen reconstructions at centennial to multi-millenial timescales, mirroring the variability gap found in the marine domain. Finally, we investigate how future climate model projections with greater internal variability would be affected, and how this increases the uncertainty of regional land temperature projections.

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