scholarly journals Temperature-dependence of aerosol optical depth over the southeastern US

2016 ◽  
Author(s):  
Tero Mielonen ◽  
Anca Hienola ◽  
Thomas Kühn ◽  
Joonas Merikanto ◽  
Antti Lipponen ◽  
...  
Keyword(s):  
Temperature Dependence ◽  
Land Surface ◽  
Radiative Forcing ◽  
Climate Model ◽  
Biogenic Emissions ◽  
Anthropogenic Emissions ◽  
Model Estimate ◽  
Model Simulations ◽  
Southeastern Us ◽  
Biogenic Aerosols

Abstract. Previous studies have indicated that summer-time aerosol optical depths (AOD) over the southeastern US are dependent on temperature but the reason for this dependence and its radiative effects have so far been unclear. To quantify these effects we utilized AOD and land surface temperature (LST) products from the Advanced Along-Track Scanning Radiometer (AATSR) with observations of tropospheric nitrogen dioxide (NO2) column densities from the Ozone Monitoring Instrument (OMI). Furthermore, simulations of the global aerosol-climate model ECHAM-HAMMOZ have been used to identify the possible processes affecting aerosol loads and their dependence on temperature over the studied region. Our results showed that the level of AOD in the southeastern US is mainly governed by anthropogenic emissions but the observed temperature dependent behaviour is most likely originating from non-anthropogenic emissions. Model simulations indicated that biogenic emissions of volatile organic compounds (BVOC) can explain the observed temperature dependence of AOD. According to the remote sensing data sets, the non-anthropogenic contribution increases AOD by approximately 0.009 ± 0.018 K−1 while the modelled BVOC emissions increase AOD by 0.022 ± 0.002 K−1. Consequently, the regional direct radiative effect (DRE) of the non-anthropogenic AOD is −0.43 ± 0.88 W/m2/K and −0.17 ± 0.35 W/m2/K for clear- and all-sky conditions, respectively. The model estimate of the regional clear-sky DRE for biogenic aerosols is also in the same range: −0.86 ± 0.06 W/m2/K. These DRE values indicate significantly larger cooling than the values reported for other forested regions. Furthermore, the model simulations showed that biogenic emissions increased the number of biogenic aerosols with radius larger than 100 nm (N100, proxy for cloud condensation nuclei) by 28 % per one degree temperature increase. For the total N100, the corresponding increase was 4 % which implies that biogenic emissions could also have a small effect on indirect radiative forcing in this region.

scholarly journals Anthropogenic forcing and response yield observed positive trend in Earth’s energy imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shiv Priyam Raghuraman ◽  
David Paynter ◽  
V. Ramaswamy
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Solar Spectrum ◽  
Internal Variability ◽  
Satellite Observations ◽  
Positive Trend ◽  
Energy Imbalance ◽  
Model Simulations ◽  
Heat Uptake ◽  
Climate Model Simulations

AbstractThe observed trend in Earth’s energy imbalance (TEEI), a measure of the acceleration of heat uptake by the planet, is a fundamental indicator of perturbations to climate. Satellite observations (2001–2020) reveal a significant positive globally-averaged TEEI of 0.38 ± 0.24 Wm−2decade−1, but the contributing drivers have yet to be understood. Using climate model simulations, we show that it is exceptionally unlikely (<1% probability) that this trend can be explained by internal variability. Instead, TEEI is achieved only upon accounting for the increase in anthropogenic radiative forcing and the associated climate response. TEEI is driven by a large decrease in reflected solar radiation and a small increase in emitted infrared radiation. This is because recent changes in forcing and feedbacks are additive in the solar spectrum, while being nearly offset by each other in the infrared. We conclude that the satellite record provides clear evidence of a human-influenced climate system.

scholarly journals The DeepMIP contribution to PMIP4: experimental design for model simulations of the EECO, PETM, and pre-PETM (version 1.0)

2017 ◽  
Vol 10 (2) ◽  
pp. 889-901 ◽  
Author(s):  
Daniel J. Lunt ◽  
Matthew Huber ◽  
Eleni Anagnostou ◽  
Michiel L. J. Baatsen ◽  
Rodrigo Caballero ◽  
...  
Keyword(s):  
Experimental Design ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Geological Data ◽  
Time Model ◽  
Model Simulations ◽  
Intercomparison Project ◽  
Climate Model Simulations

Abstract. Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( >  800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene ( ∼  50  Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4 ×  CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP – the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

scholarly journals Evaluating Climate Model Simulations of the Radiative Forcing and Radiative Response at Earth’s Surface

2019 ◽  
Vol 32 (13) ◽  
pp. 4089-4102 ◽  
Author(s):  
Ryan J. Kramer ◽  
Brian J. Soden ◽  
Angeline G. Pendergrass
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Lapse Rate ◽  
Radiation Budget ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract We analyze the radiative forcing and radiative response at Earth’s surface, where perturbations in the radiation budget regulate the atmospheric hydrological cycle. By applying a radiative kernel-regression technique to CMIP5 climate model simulations where CO2 is instantaneously quadrupled, we evaluate the intermodel spread in surface instantaneous radiative forcing, radiative adjustments to this forcing, and radiative responses to surface warming. The cloud radiative adjustment to CO2 forcing and the temperature-mediated cloud radiative response exhibit significant intermodel spread. In contrast to its counterpart at the top of the atmosphere, the temperature-mediated cloud radiative response at the surface is found to be positive in some models and negative in others. Also, the compensation between the temperature-mediated lapse rate and water vapor radiative responses found in top-of-atmosphere calculations is not present for surface radiative flux changes. Instantaneous radiative forcing at the surface is rarely reported for model simulations; as a result, intermodel differences have not previously been evaluated in global climate models. We demonstrate that the instantaneous radiative forcing is the largest contributor to intermodel spread in effective radiative forcing at the surface. We also find evidence of differences in radiative parameterizations in current models and argue that this is a significant, but largely overlooked, source of bias in climate change simulations.

scholarly journals Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation

2019 ◽  
Vol 12 (7) ◽  
pp. 3017-3043 ◽  
Author(s):  
Sihan Li ◽  
David E. Rupp ◽  
Linnia Hawkins ◽  
Philip W. Mote ◽  
Doug McNeall ◽  
...  
Keyword(s):  
Parameter Space ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Model ◽  
Bias Reduction ◽  
Sensitivity Analyses ◽  
Model Framework ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental midlatitudes. This work, with the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multiphased parameter refinement approach, particularly over the northwestern United States (NWUS). Each phase consists of (1) creating a perturbed parameter ensemble with the coupled global–regional atmospheric model, (2) building statistical emulators that estimate climate metrics as functions of parameter values, (3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, as well as land surface parameters that affect plant photosynthesis, transpiration, and evaporation, but also suggest that this iterative approach to perturbed parameters has an important role to play in the evolution of physical parameterizations.

scholarly journals What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?

2014 ◽  
Vol 11 (10) ◽  
pp. 2601-2622 ◽  
Author(s):  
M. Liu ◽  
K. Rajagopalan ◽  
S. H. Chung ◽  
X. Jiang ◽  
J. Harrison ◽  
...  
Keyword(s):  
Crop Yield ◽  
Bias Correction ◽  
Land Surface ◽  
Climate Model ◽  
Biogenic Emissions ◽  
Climate Data ◽  
Post Processing ◽  
Trade Offs ◽  
Impact Models ◽  
No Emissions

Abstract. Regional climate change impact (CCI) studies have widely involved downscaling and bias correcting (BC) global climate model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables (evapotranspiration (ET), runoff, snow water equivalent (SWE), and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ-Andrews). Simulation results from the coupled ECHAM5–MPI-OM model with A1B emission scenario were first dynamically downscaled to 12 km resolution with the WRF model. Then a quantile-mapping-based statistical downscaling model was used to downscale them into 1/16° resolution daily climate data over historical and future periods. Two climate data series were generated, with bias correction (BC) and without bias correction (NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological data sets. These impact models include a macroscale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrological model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ-Andrews's ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at regional scales. We conclude that there are trade-offs between using BC climate data for offline CCI studies versus directly modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; for example, BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality, for which VOCs are a primary indicator.

scholarly journals Soil moisture–atmosphere feedback dominates land carbon uptake variability

Nature ◽  
2021 ◽  
Vol 592 (7852) ◽  
pp. 65-69
Author(s):  
Vincent Humphrey ◽  
Alexis Berg ◽  
Philippe Ciais ◽  
Pierre Gentine ◽  
Martin Jung ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Dominant Role ◽  
Vapour Pressure Deficit ◽  
Terrestrial Ecosystems ◽  
Surface Model ◽  
Carbon Uptake ◽  
Model Simulations

AbstractYear-to-year changes in carbon uptake by terrestrial ecosystems have an essential role in determining atmospheric carbon dioxide concentrations1. It remains uncertain to what extent temperature and water availability can explain these variations at the global scale2–5. Here we use factorial climate model simulations6 and show that variability in soil moisture drives 90 per cent of the inter-annual variability in global land carbon uptake, mainly through its impact on photosynthesis. We find that most of this ecosystem response occurs indirectly as soil moisture–atmosphere feedback amplifies temperature and humidity anomalies and enhances the direct effects of soil water stress. The strength of this feedback mechanism explains why coupled climate models indicate that soil moisture has a dominant role4, which is not readily apparent from land surface model simulations and observational analyses2,5. These findings highlight the need to account for feedback between soil and atmospheric dryness when estimating the response of the carbon cycle to climatic change globally5,7, as well as when conducting field-scale investigations of the response of the ecosystem to droughts8,9. Our results show that most of the global variability in modelled land carbon uptake is driven by temperature and vapour pressure deficit effects that are controlled by soil moisture.

scholarly journals Radiative forcing by volcanic eruptions since 1990, calculated with a chemistry-climate model and a new emission inventory based on vertically resolved satellite measurements

2021 ◽  
Author(s):  
Jennifer Schallock ◽  
Christoph Brühl ◽  
Christine Bingen ◽  
Michael Höpfner ◽  
Landon Rieger ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Emission Inventory ◽  
Climate Model ◽  
Aerosol Particles ◽  
Volcanic Eruptions ◽  
Stratospheric Aerosol ◽  
Radiation Budget ◽  
Vertical Profiles ◽  
Satellite Measurements ◽  
Model Simulations

Abstract. This paper presents model simulations of stratospheric aerosols with a focus on explosive volcanic eruptions. Using various (occulation and limb based) satellite instruments, with vertical profiles of sulfur dioxide (SO2) from the MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument and vertical profiles of aerosol extinction from GOMOS (Global Ozone Monitoring by Occultation of Stars), OSIRIS (Optical Spectrograph and InfraRed Imaging System), and SAGE II (Stratospheric Aerosol and Gas Experiment), we characterised the influence of volcanic aerosols for the period between 1990 and 2019. We established a volcanic sulfur emission inventory that includes more than 500 eruptions. The identified SO2 perturbations were incorporated as three-dimensional pollution plumes into a chemistry-climate model, which converts the gases into aerosol particles and computes their optical properties. The Aerosol Optical Depth (AOD) and the climate radiative forcing are calculated online. Combined with model improvements, the simulations reproduce the observations of the various satellites. Slight deviations between the observations and model simulations were found only for the large volcanic eruption of Pinatubo in 1991. This is likely due to either an overestimation of the removal of aerosol particles in the model, or limitations of the satellite measurements, which are related to saturation effects associated with anomalously high aerosol concentrations. Since Pinatubo, only smaller-sized volcanic eruptions have taken place. Weak- and medium-strength volcanic eruptions captured in satellite data and the Smithsonian database typically inject about 10 kt to 50 kt SO2 directly into the upper troposphere/lower stratosphere (UTLS) region or transport it indirectly via convection and advection. Our results show that these relatively smaller eruptions, which occur quite frequently, can nevertheless contribute significantly to the stratospheric aerosol layer and are relevant for the Earth's radiation budget. These eruptions are found to cause a global radiative forcing in the order of −0.1 Wm−2 at the tropopause.

Applying Scaled Vegetation Greenness Metrics to Constrain Simulated Transpiration Anomalies: A Study over Australia*

2013 ◽  
Vol 15 (4) ◽  
pp. 1607-1623 ◽  
Author(s):  
Mark Decker ◽  
Andy J. Pitman ◽  
Jason Evans
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Land Surface Models ◽  
Model Simulations ◽  
Vegetation Greenness ◽  
Surface Models ◽  
The Mean ◽  
Climate Model Simulations

Abstract The feasibility of using vegetation greenness metrics as a proxy for transpiration variability over Australia is demonstrated. Several global evapotranspiration datasets, one of which provides transpiration data and is constructed independently of the vegetation greenness measurements, are compared to four satellite-based observations representative of the state of the vegetation over several regions in Australia. Further estimates of the transpiration are obtained by decomposing the evapotranspiration datasets using an ensemble of land surface model simulations. On monthly time scales, the greenness anomaly metrics show a near one-to-one relationship with the transpiration estimates when the time series are appropriately scaled by the mean. The authors demonstrate that anomalous vegetation greenness metrics, when properly scaled, provide a tool for evaluating transpiration variability simulated by land surface models and observation-based evapotranspiration datasets that include transpiration. These methods provide a new test to help constrain the dynamic behavior of the land surface in climate model simulations.

scholarly journals Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics

2017 ◽  
Vol 8 (2) ◽  
pp. 387-403 ◽  
Author(s):  
Sebastian Sippel ◽  
Jakob Zscheischler ◽  
Miguel D. Mahecha ◽  
Rene Orth ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Global Climate Model ◽  
Large Fraction ◽  
Cmip5 Models ◽  
Temperature Extremes ◽  
Model Ensemble ◽  
Model Simulations

Abstract. The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter. This coupled behaviour causes various land–atmosphere feedbacks, and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface in exacerbating summer heat waves in midlatitude regions has been identified empirically for high-impact heat waves, but individual climate models differ widely in their respective representation of land–atmosphere coupling. Here, we compile an ensemble of 54 combinations of observations-based temperature (T) and evapotranspiration (ET) benchmarking datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land–atmosphere interactions during periods of anomalously warm temperatures. First, we demonstrate that a large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in midlatitude regions during the warm season and in several tropical regions year-round. These coincidences (high T, low ET) are closely related to the representation of temperature variability and extremes across the multi-model ensemble. Second, we derive a land-coupling constraint based on the spread of the T–ET datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these benchmark estimates. The constrained multi-model simulations exhibit more realistic temperature extremes of reduced magnitude in present climate in regions where models show substantial spread in T–ET coupling, i.e. biases in the model ensemble are consistently reduced. Also the multi-model simulations for the coming decades display decreased absolute temperature extremes in the constrained ensemble. On the other hand, the differences between projected and present-day climate extremes are affected to a lesser extent by the applied constraint, i.e. projected changes are reduced locally by around 0.5 to 1 °C – but this remains a local effect in regions that are highly sensitive to land–atmosphere coupling. In summary, our approach offers a physically consistent, diagnostic-based avenue to evaluate multi-model ensembles and subsequently reduce model biases in simulated and projected extreme temperatures.

scholarly journals Biases in the albedo sensitivity to deforestation in CMIP5 models and their impacts on the associated historical Radiative Forcing

2020 ◽  
Author(s):  
Quentin Lejeune ◽  
Edouard L. Davin ◽  
Grégory Duveiller ◽  
Bas Crezee ◽  
Ronny Meier ◽  
...  
Keyword(s):  
Land Cover ◽  
Radiative Forcing ◽  
Climate Model ◽  
Coupled Model ◽  
Climate Impact ◽  
Cmip5 Models ◽  
Land Cover Changes ◽  
Model Simulations ◽  
Land Cover Types ◽  
Conversion Rates

Abstract. Climate model biases in the representation of albedo variations between land cover types contribute to uncertainties on the climate impact of land cover changes since pre-industrial times, and especially on the associated Radiative Forcing. The recent publications of new observation-based datasets offer opportunities to investigate these biases and their impact on historical albedo changes in simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Conducting such an assessment is however complicated by the non-availability of albedo values for specific land cover types, as well as the limited number of simulations isolating the land use forcing in CMIP. In this study, we demonstrate the suitability of a new methodology to extract the albedo of trees and crops/grasses in standard climate model simulations. We then apply it to historical runs from 13 CMIP5 models and compare the obtained results to satellite-derived reference data. This allows us to identify substantial biases in the representation of the albedo of trees, crops/grasses, and the albedo change due to the transition between these two land cover types in the analysed models. Additionally, we reconstruct the local albedo changes induced by historical conversions between trees and crops/grasses for 15 CMIP5 models. This allows us to derive estimates of the Radiative Forcing from land cover changes since pre-industrial times ranging between 0 and −0.22 W/m2, with a mean value of −0.07 W/m2. Constraining the albedo response to transitions between trees and crops/grasses from the models with satellite-derived data leads to an increase in this range, however after excluding two models with unrealistic conversion rates from trees to crops/grasses we obtain a revised model mean estimate of −0.11 W/m2 (with individual model results between −0.04 and −0.16 W/m2). These numbers are at the lower end of the range provided by the IPCC AR5 (−0.15 ± 0.10 W/m2). The approach described in this study can be applied on other model simulations, such as those from CMIP6, especially as a diagnostic enabling the reproduction of the model evaluation part has been included in the ESMValTool v2.0.

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