scholarly journals The Land Surface Contribution to the Potential Predictability of Boreal Summer Season Climate

2005 ◽  
Vol 6 (5) ◽  
pp. 618-632 ◽  
Author(s):  
Paul A. Dirmeyer
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Climate Model ◽  
Initial Conditions ◽  
Surface Fluxes ◽  
Boreal Summer ◽  
Potential Predictability ◽  
Noise Component ◽  
Near Surface ◽  
Soil Wetness

Abstract The role of the land surface in contributing to the potential predictability of the boreal summer climate is investigated with a coupled land–atmosphere climate model. Ensemble simulations for 1982–99 have been conducted with specified observed sea surface temperatures (SSTs). Several treatments of the land surface are investigated: climatological land surface initialization, realistic initialization of soil wetness, and a series of experiments where downward surface fluxes over land are replaced with observed proxies of precipitation, shortwave, and longwave radiation. Without flux replacement the model exhibits strong drift in soil wetness and both systematic errors and poor simulation of interannual variations of precipitation and near-surface temperature. With flux replacement there are large improvements in simulation of both spatial patterns and interannual variability of precipitation and surface temperature. The land surface apparently does contribute, through positive feedback with the atmosphere, to regional climate anomalies. However, because of the sizeable noise component in precipitation, the strong land–atmosphere feedback may not translate into reliable enhancements in predictability, particularly in years of weak anomalies in the land surface initial conditions at the start of boreal summer.

scholarly journals Flux Replacement as a Method to Diagnose Coupled Land–Atmosphere Model Feedback

2004 ◽  
Vol 5 (6) ◽  
pp. 1034-1048 ◽  
Author(s):  
Paul A. Dirmeyer ◽  
Mei Zhao
Keyword(s):  
Surface State ◽  
Land Surface ◽  
Climate Model ◽  
Initial Conditions ◽  
Systematic Errors ◽  
Surface Fluxes ◽  
Boreal Summer ◽  
Near Surface ◽  
Soil Wetness ◽  
The Impact

Abstract The potential role of the land surface state in improving predictions of seasonal climate is investigated with a coupled land–atmosphere climate model. Climate simulations for 18 boreal-summer seasons (1982–99) have been conducted with specified observed sea surface temperature (SST). The impact on prediction skill of the initial land surface state (interannually varying versus climatological soil wetness) and the effect of errors in downward surface fluxes (precipitation and longwave/shortwave radiation) over land are investigated with a number of parallel experiments. Flux errors are addressed by replacing the downward fluxes with observed values in various combinations to ascertain the separate roles of water and energy flux errors on land surface state variables, upward water and energy fluxes from the land surface, and the important climate variables of precipitation and near-surface air temperature. Large systematic errors are found in the model, which are only mildly alleviated by the specification of realistic initial soil wetness. The model shows little skill in simulating seasonal anomalies of precipitation, but it does have skill in simulating temperature variations. Replacement of the downward surface fluxes has a clear positive impact on systematic errors, suggesting that the land–atmosphere feedback is helping to exacerbate climate drift. Improvement in the simulation of year-to-year variations in climate is even more evident. With flux replacement, the climate model simulates temperature anomalies with considerable skill over nearly all land areas, and a large fraction of the globe shows significant skill in the simulation of precipitation anomalies. This suggests that the land surface can communicate climate anomalies back to the atmosphere, given proper meteorological forcing. Flux substitution appears to have the largest benefit to improving precipitation skill over the Northern Hemisphere midlatitudes, whereas use of realistic land surface initial conditions improves skill to significant levels over regions of the Southern Hemisphere. Correlations between sets of integrations show that the model has a robust and systematic global response to SST anomalies.

scholarly journals The Hydrologic Feedback Pathway for Land–Climate Coupling

2006 ◽  
Vol 7 (5) ◽  
pp. 857-867 ◽  
Author(s):  
Paul A. Dirmeyer
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Water Cycle ◽  
Initial Conditions ◽  
Global Climate ◽  
Global Climate Model ◽  
Signal Transmission ◽  
Hydrologic Cycle ◽  
Boreal Summer ◽  
The Impact

Abstract The impact of improvements in land surface initialization and specification of observed rainfall in global climate model simulations of boreal summer are examined to determine how the changes propagate around the hydrologic cycle in the coupled land–atmosphere system. On the global scale, about 70% of any imparted signal in the hydrologic cycle is lost in the transition from atmosphere to land, and 70% of the remaining signal is lost from land to atmosphere. This means that globally, less than 10% of the signal of any change survives the complete circuit of the hydrologic cycle in this model. Regionally, there is a great deal of variability. Specification of observed precipitation to the land component of the climate model strongly communicates its signal to soil wetness in rainy regions, but predictive skill in evapotranspiration arises primarily in dry regions. A maximum in signal transmission to model precipitation exists in between, peaking where mean rainfall rates are 1.5–2 mm day−1. It appears that the nature of the climate system inherently limits to these regions the potential impact on prediction of improvements in the ability of models to simulate the water cycle. Land initial conditions impart a weaker signal on the system than replacement of precipitation, so a weaker response is realized in the system, focused mainly in dry regions.

scholarly journals Influence of “Realistic” Land Surface Wetness on Predictability of Seasonal Precipitation in Boreal Summer

2006 ◽  
Vol 19 (8) ◽  
pp. 1450-1460 ◽  
Author(s):  
Shinjiro Kanae ◽  
Yukiko Hirabayashi ◽  
Tomohito Yamada ◽  
Taikan Oki
Keyword(s):  
Land Surface ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
The Other ◽  
Boreal Summer ◽  
Seasonal Precipitation ◽  
Potential Predictability ◽  
Surface Wetness ◽  
Semiarid Regions

Abstract Outputs from two ensembles of atmospheric model simulations for 1951–98 define the influence of “realistic” land surface wetness on seasonal precipitation predictability in boreal summer. The ensembles consist of one forced with observed sea surface temperatures (SSTs) and the other forced with realistic land surface wetness as well as SSTs. Predictability was determined from correlations between the time series of simulated and observed precipitation. The ratio of forced variance to total variance determined potential predictability. Predictability occurred over some land areas adjacent to tropical oceans without land wetness forcing. On the other hand, because of the chaotic nature of the atmosphere, considerable parts of the land areas of the globe did not even show potential predictability with both land wetness and SST forcings. The use of land wetness forcing enhanced predictability over semiarid regions. Such semiarid regions are generally characterized by a negative correlation between fluxes of latent heat and sensible heat from the land surface, and are “water-regulating” areas where soil moisture plays a governing role in land–atmosphere interactions. Actual seasonal prediction may be possible in these regions if slowly varying surface conditions can be estimated in advance. In contrast, some land regions (e.g., south of the Sahel, the Amazon, and Indochina) showed little predictability despite high potential predictability. These regions are mostly characterized by a positive correlation between the surface fluxes, and are “radiation-regulating” areas where the atmosphere plays a leading role. Improvements in predictability for these regions may require further improvements in model physics.

scholarly journals Examining the Interaction of Growing Crops with Local Climate Using a Coupled Crop–Climate Model

2009 ◽  
Vol 22 (6) ◽  
pp. 1393-1411 ◽  
Author(s):  
Tom Osborne ◽  
Julia Slingo ◽  
David Lawrence ◽  
Tim Wheeler
Keyword(s):  
Climate Variability ◽  
Crop Yield ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Crop Growth ◽  
Surface Fluxes ◽  
Near Surface ◽  
Local Climate ◽  
The Impact

Abstract This paper examines to what extent crops and their environment should be viewed as a coupled system. Crop impact assessments currently use climate model output offline to drive process-based crop models. However, in regions where local climate is sensitive to land surface conditions more consistent assessments may be produced with the crop model embedded within the land surface scheme of the climate model. Using a recently developed coupled crop–climate model, the sensitivity of local climate, in particular climate variability, to climatically forced variations in crop growth throughout the tropics is examined by comparing climates simulated with dynamic and prescribed seasonal growth of croplands. Interannual variations in land surface properties associated with variations in crop growth and development were found to have significant impacts on near-surface fluxes and climate; for example, growing season temperature variability was increased by up to 40% by the inclusion of dynamic crops. The impact was greatest in dry years where the response of crop growth to soil moisture deficits enhanced the associated warming via a reduction in evaporation. Parts of the Sahel, India, Brazil, and southern Africa were identified where local climate variability is sensitive to variations in crop growth, and where crop yield is sensitive to variations in surface temperature. Therefore, offline seasonal forecasting methodologies in these regions may underestimate crop yield variability. The inclusion of dynamic crops also altered the mean climate of the humid tropics, highlighting the importance of including dynamical vegetation within climate models.

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 Assessing the Impact of Changes in Land Surface Conditions on WRF Predictions in Arid Regions

2020 ◽  
Vol 21 (12) ◽  
pp. 2829-2853 ◽  
Author(s):  
Marouane Temimi ◽  
Ricardo Fonseca ◽  
Narendra Nelli ◽  
Michael Weston ◽  
Mohan Thota ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Soil Texture ◽  
Land Surface ◽  
Surface Wind ◽  
Surface Fluxes ◽  
Abu Dhabi ◽  
Horizontal Wind ◽  
Cold Bias ◽  
Near Surface ◽  
The Impact

AbstractA thorough evaluation of the Weather Research and Forecasting (WRF) Model is conducted over the United Arab Emirates, for the period September 2017–August 2018. Two simulations are performed: one with the default model settings (control run), and another one (experiment) with an improved representation of soil texture and land use land cover (LULC). The model predictions are evaluated against observations at 35 weather stations, radiosonde profiles at the coastal Abu Dhabi International Airport, and surface fluxes from eddy-covariance measurements at the inland city of Al Ain. It is found that WRF’s cold temperature bias, also present in the forcing data and seen almost exclusively at night, is reduced when the surface and soil properties are updated, by as much as 3.5 K. This arises from the expansion of the urban areas, and the replacement of loamy regions with sand, which has a higher thermal inertia. However, the model continues to overestimate the strength of the near-surface wind at all stations and seasons, typically by 0.5–1.5 m s−1. It is concluded that the albedo of barren/sparsely vegetated regions in WRF (0.380) is higher than that inferred from eddy-covariance observations (0.340), which can also explain the referred cold bias. At the Abu Dhabi site, even though soil texture and LULC are not changed, there is a small but positive effect on the predicted vertical profiles of temperature, humidity, and horizontal wind speed, mostly between 950 and 750 hPa, possibly because of differences in vertical mixing.

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

scholarly journals Variational Assimilation of Land Surface Temperature within the ORCHIDEE Land Surface Model Version 1.2.6

2016 ◽  
Author(s):  
H. S. Benavides Pinjosovsky ◽  
S. Thiria ◽  
C. Ottlé ◽  
J. Brajard ◽  
F. Badran ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Adjoint Model ◽  
Variational Assimilation ◽  
Internal Parameters

Abstract. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software denoted YAO was used as a framework to implement a 4D-VAR assimilation method. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the eleven most influent internal parameters of SECHIBA or of the initial conditions of the soil water content by observing the land surface temperature measured in situ or as it could be observed by remote sensing as brightness temperature. The paper presents the fundamental principles of the 4D-Var assimilation, the semi-generator software YAO and some experiments showing the accuracy of the adjoint code distributed. In addition, a distributed version is available when only the land surface temperature is observed.

Improving the global modeling of soils in JULES and the Unified Model: Updating from UM/HWSD to SoilGrids soil properties and from the Brooks & Corey to the van Genuchten soil-hydraulics model

2021 ◽  
Author(s):  
Patrick C. McGuire ◽  
Pier Luigi Vidale ◽  
Martin J. Best ◽  
David H. Case ◽  
Imtiaz Dharssi ◽  
...  
Keyword(s):  
Soil Properties ◽  
Land Surface ◽  
Climate Model ◽  
Model Updating ◽  
Unified Model ◽  
Pedotransfer Functions ◽  
Hydraulic Parameters ◽  
Near Surface ◽  
Soil Database ◽  
Soil Hydraulic

<p>    We have updated the soil properties used in JULES (Joint UK Land Environment Simulator), which is the land-surface component of the UM (Unified Model, the UK Met Office’s climate model). JULES models the interaction of the land surface with the atmosphere, and simulates the energy, water, and carbon fluxes. JULES allows either: (i) the Brooks & Corey (BC) model for estimating soil hydraulic properties, or (ii) the van Genuchten (VG) model but using hydraulic parameters translated from the BC model. One advantage of the VG model over the BC model is the smoother dependence of water retention upon matric potential for nearly saturated soils. Herein, we report on our work towards fully implementing the VG model in JULES and in the UM, through the implementation and evaluation of several VG pedotransfer functions (PTFs) for estimating the soil hydraulic parameters used in the hydraulic functions.</p> <p>    We have tested three VG PTFs in global offline JULES runs (driven with WFDEI data over 1979-2012): the combination of Tóth et al. PTFs 17 & 20, the Weynants et al. PTF, and the Zhang & Schaap ROSETTA3 H1 PTF (modified for sandy soils). We also modernized the soil basic properties that are conventionally used for JULES and the UM, from the UM version of the Harmonized World Soil Database (HWSD) to the SoilGrids database.</p> <p>    Evaluation of JULES simulations shows (i) that the modified version of the Zhang & Schaap ROSETTA3 H1 PTF is the best VG option, and (ii) that it compares favorably with the BC control model (which uses the Cosby et al. PTF and the UM/HWSD soils), in terms of the surface energy balance and the mitigation of near-surface temperature biases over mid-latitude continental regions. This modified version of the Zhang & Schaap ROSETTA3 H1 PTF with SoilGrids soils is also currently being used in coupled land-atmosphere UM runs.</p>

scholarly journals The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction

2016 ◽  
Vol 12 (7) ◽  
pp. 1519-1538 ◽  
Author(s):  
Harry Dowsett ◽  
Aisling Dolan ◽  
David Rowley ◽  
Robert Moucha ◽  
Alessandro M. Forte ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Land Surface ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Sea Surface ◽  
Paleoenvironmental Reconstruction ◽  
Dynamic Topography ◽  
Intercomparison Project

Abstract. The mid-Piacenzian is known as a period of relative warmth when compared to the present day. A comprehensive understanding of conditions during the Piacenzian serves as both a conceptual model and a source for boundary conditions as well as means of verification of global climate model experiments. In this paper we present the PRISM4 reconstruction, a paleoenvironmental reconstruction of the mid-Piacenzian ( ∼  3 Ma) containing data for paleogeography, land and sea ice, sea-surface temperature, vegetation, soils, and lakes. Our retrodicted paleogeography takes into account glacial isostatic adjustments and changes in dynamic topography. Soils and lakes, both significant as land surface features, are introduced to the PRISM reconstruction for the first time. Sea-surface temperature and vegetation reconstructions are unchanged but now have confidence assessments. The PRISM4 reconstruction is being used as boundary condition data for the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) experiments.

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