Simulated Interannual Variability Research Articles

Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.

Various ways of using empirical orthogonal functions for climate model evaluation

Abstract. We present a framework for evaluating multi-model ensembles based on common empirical orthogonal functions (common EOFs) that emphasize salient features connected to spatio-temporal covariance structures embedded in large climate data volumes. This framework enables the extraction of the most pronounced spatial patterns of coherent variability within the joint dataset and provides a set of weights for each model in terms of the principal components which refer to exactly the same set of spatial patterns of covariance. In other words, common EOFs provide a means for extracting information from large volumes of data. Moreover, they can provide an objective basis for evaluation that can be used to accentuate ensembles more than traditional methods for evaluation, which tend to focus on individual models. Our demonstration of the capability of common EOFs reveals a statistically significant improvement of the sixth generation of the World Climate Research Programme (WCRP) Climate Model Intercomparison Project (CMIP6) simulations in comparison to the previous generation (CMIP5) in terms of their ability to reproduce the mean seasonal cycle in air surface temperature, precipitation, and mean sea level pressure over the Nordic countries. The leading common EOF principal component for annually or seasonally aggregated temperature, precipitation, and pressure statistics suggests that their simulated interannual variability is generally consistent with that seen in the ERA5 reanalysis. We also demonstrate how common EOFs can be used to analyse whether CMIP ensembles reproduce the observed historical trends over the historical period 1959–2021, and the results suggest that the trend statistics provided by both CMIP5 RCP4.5 and CMIP6 SSP245 are consistent with observed trends. An interesting finding is also that the leading common EOF principal component for annually or seasonally aggregated statistics seems to be approximately normally distributed, which is useful information about the multi-model ensemble data.

CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5)

Abstract. The inclusion of perennial, woody crops in land surface models (LSMs) is crucial for addressing their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantify the biogeochemical and biogeophysical processes associated with these agroecosystems, we developed and tested a new sub-model, CLM5-FruitTree, for deciduous fruit orchards within the framework of the Community Land Model version 5 (CLM5). The model development included (1) a new perennial crop phenology description, (2) an adapted C and nitrogen allocation scheme, considering both storage and photosynthetic growth of annual and perennial plant organs, (3) typical management practices associated with fruit orchards, and (4) the parameterization of an apple plant functional type. CLM5-FruitTree was tested using extensive field measurements from an apple orchard in South Tyrol, Italy. Growth and partitioning of biomass to the individual plant components were well represented by CLM5-FruitTree, and average yield was predicted within 2.3 % of the observed values despite low simulated inter-annual variability compared to observations. The simulated seasonal course of C, energy, and water fluxes was in good agreement with the eddy covariance (EC) measurements owing to the accurate representation of the prolonged growing season and typical leaf area development of the orchard. We found that gross primary production, net radiation, and latent heat flux were highly correlated (r>0.94) with EC measurements and showed little bias (<±5 %). Simulated respiration components, sensible heat, and soil heat flux were less consistent with observations. This was attributed to simplifications in the orchard structure and to the presence of additional management practices that are not yet represented in CLM5-FruitTree. Finally, the results suggested that the representation of microbial and autotrophic respiration and energy partitioning in complex, discontinuous canopies in CLM5 requires further attention. The new CLM5-FruitTree sub-model improved the representation of agricultural systems in CLM5 and can be used to study land surface processes in fruit orchards at the local, regional, or larger scale.

Contrasting the Role of Regional and Remote Circulation in Driving Asian Monsoon Biases in MetUM GA7.1

AbstractMonsoon precipitation affects nearly half of the world's population, but monsoon biases are a long‐standing problem in climate simulations. We apply dynamical nudging either globally or regionally to demonstrate the role of regional and remote circulation in generating Asian monsoon biases in an atmospheric general circulation model. Monsoon precipitation biases are substantially reduced in response to global nudging but may also be exacerbated over the warm oceanic equatorial areas because of unconstrained sub‐grid convection. Regional nudging over Asia appears to be more efficient than nudging outside Asia in reducing seasonal precipitation biases over eastern China and India. This suggests a predominant role of local circulation anomalies in generating monsoon precipitation errors in these regions. An exception is the summer precipitation bias over eastern China, which is more strongly controlled by remote circulation. Besides seasonal mean rainfall, nudging can also improve the simulated interannual and intraseasonal precipitation variability over the subtropics. This results in a better skill in reproducing the observed El Niño teleconnections to India and China and the monsoon onset date. Improved understanding of the origin of Asian monsoon biases and the contribution from regional and remote circulation advances our knowledge of the interplay between the Asian monsoon and large‐scale circulation, which can be beneficial to the simulation and interpretation of monsoon projections.

Global and regional impacts of land cover changes on isoprene emissions derived from spaceborne data and the MEGAN model

Abstract. Among the biogenic volatile organic compounds (BVOCs) emitted by plant foliage, isoprene is by far the most important in terms of both global emission and atmospheric impact. It is highly reactive in the air, and its degradation favours the generation of ozone (in the presence of NOx) and secondary organic aerosols. A critical aspect of BVOC emission modelling is the representation of land use and land cover (LULC). The current emission inventories are usually based on land cover maps that are either modelled and dynamic or satellite-based and static. In this study, we use the state-of-the-art Model of Emissions of Gases and Aerosols from Nature (MEGAN) model coupled with the canopy model MOHYCAN (Model for Hydrocarbon emissions by the CANopy) to generate and evaluate emission inventories relying on satellite-based LULC maps at annual time steps. To this purpose, we first intercompare the distribution and evolution (2001–2016) of tree coverage from three global satellite-based datasets, MODerate resolution Imaging Spectroradiometer (MODIS), ESA Climate Change Initiative Land Cover (ESA CCI-LC), and the Global Forest Watch (GFW), and from national inventories. Substantial differences are found between the datasets; e.g. the global areal coverage of trees ranges from 30 to 50×106 km2, with trends spanning from −0.26 to +0.03 % yr−1 between 2001 and 2016. At the national level, the increasing trends in forest cover reported by some national inventories (in particular for the US) are contradicted by all remotely sensed datasets. To a great extent, these discrepancies stem from the plurality of definitions of forest used. According to some local censuses, clear cut areas and seedling or young trees are classified as forest, while satellite-based mappings of trees rely on a minimum height. Three inventories of isoprene emissions are generated, differing only in their LULC datasets used as input: (i) the static distribution of the stand-alone version of MEGAN, (ii) the time-dependent MODIS land cover dataset, and (iii) the MODIS dataset modified to match the tree cover distribution from the GFW database. The mean annual isoprene emissions (350–520 Tg yr−1) span a wide range due to differences in tree distributions, especially in isoprene-rich regions. The impact of LULC changes is a mitigating effect ranging from 0.04 to 0.33 % yr−1 on the positive trends (0.94 % yr−1) mainly driven by temperature and solar radiation. This study highlights the uncertainty in spatial distributions of and temporal variability in isoprene associated with remotely sensed LULC datasets. The interannual variability in the emissions is evaluated against spaceborne observations of formaldehyde (HCHO), a major isoprene oxidation product, through simulations using the global chemistry transport model (CTM) IMAGESv2. A high correlation (R > 0.8) is found between the observed and simulated interannual variability in HCHO columns in most forested regions. The implementation of LULC change has little impact on this correlation due to the dominance of meteorology as a driver of short-term interannual variability. Nevertheless, the simulation accounting for the large tree cover declines of the GFW database over several regions, notably Indonesia and Mato Grosso in Brazil, provides the best agreement with the HCHO column trends observed by the Ozone Monitoring Instrument (OMI). Overall, our study indicates that the continuous tree cover fields at fine resolution provided by the GFW database are our preferred choice for constraining LULC (in combination with discrete LULC maps such as those of MODIS) in biogenic isoprene emission models.

Assessing groundwater irrigation sustainability in the Euro-Mediterranean region with an integrated agro-hydrologic model

Abstract. We assess the sustainability of groundwater irrigation in the Euro-Mediterranean region. After analysing the available data on groundwater irrigation, we identify areas where irrigation causes groundwater depletion. To prevent the latter, we experiment with guidelines to restrict groundwater irrigation to sustainable levels, simulating beneficial and detrimental impacts in terms of improved environmental flow conditions and crop yield losses. To carry out these analyses, we apply the integrated model of water resources, irrigation and crop production LISFLOOD-EPIC. Crop growth is simulated accounting for atmospheric conditions and abiotic stress factors, including transpiration deficit. Four irrigation methods are modelled: drip, sprinkler, and intermittent and permanent flooding. Hydrologic and agricultural modules are dynamically coupled at the daily time scale through soil moisture, plant water uptake, and irrigation water abstraction and application. Water abstractions of other sectors are simulated based on requirement data. Water may be withdrawn from groundwater, rivers, lakes and reservoirs. As groundwater is abstracted to buffer the effects of drought, we use groundwater depletion to detect unsustainable water exploitation. We characterise reported data of annual groundwater abstractions for irrigation available at country and sub-national levels. Country data are the most complete, but their spatial resolution is often coarse. While the resolution of sub-national data is finer, their coverage is heterogeneous. Simulated and reported irrigation groundwater abstractions compare well in several areas, particularly in France, while some structural discrepancies emerge: simulated values tend to be larger than those reported, especially in southern Spain; and simulated inter-annual variability is significantly smaller than reported in some areas, most remarkably in Turkey. Potential causes of these discrepancies are simplified model assumptions influencing irrigation frequency and amounts; lack of high temporal and spatial resolution data on irrigated areas, and irrigation technologies and distribution; and possible unreported abstractions in areas where groundwater irrigation is significant. We identify areas undergoing groundwater depletion from model simulations. In the southern Iberian Peninsula, Greece, Middle East and northern Africa, most simulated depletion is caused by irrigation. In other Mediterranean areas, depletion is caused by all sectors combined. From well measurements of groundwater table depth in Spain, we find statistically significant decline rates affecting large areas of the south, thus in agreement with the model, but also areas in the north-eastern and central parts where model estimates detect no depletion. The comparison of model- and well-based depletion rates is limited by spatial scale differences and groundwater model assumptions, for which we suggest potential research directions. We design rules restricting irrigation groundwater abstraction to prevent groundwater depletion and minimise severe irrigation shortages. We optimise them and simulate their effects in the southern Iberian Peninsula. Irrigation restrictions cause crop yield reductions in groundwater-dependent irrigated areas, particularly in the Algarve and Segura river basin districts. At the same time, they positively impact environmental flows. This study shows the potential of integrated agro-hydrologic modelling for detecting water resources over-exploitation and exploring trade-offs between crop production, sustainable irrigation and ecosystem support.

Mean State Biases and Interannual Variability Affect Perceived Sensitivities of the Madden‐Julian Oscillation to Air‐Sea Coupling

AbstractAtmosphere‐ocean feedbacks often improve the Madden‐Julian oscillation (MJO) in climate models, but these improvements are balanced by mean state biases that can degrade the MJO through changing the basic state on which the MJO operates. The Super‐Parameterized Community Atmospheric Model (SPCAM3) produces perhaps the best representation of the MJO among contemporary models, which improves further in a coupled configuration (SPCCSM3) despite considerable mean state biases in tropical sea surface temperatures and rainfall. We implement an atmosphere‐ocean‐mixed‐layer configuration of SPCAM3 (SPCAM3‐KPP) and use a flux‐correction technique to isolate the effects of coupling and mean state biases on the MJO. When constrained to the observed ocean mean state, air‐sea coupling does not substantially alter the MJO in SPCAM3, in contrast to previous studies. When constrained to the SPCCSM ocean mean state, SPCAM3‐KPP fails to produce an MJO, in stark contrast to the strong MJO in SPCCSM3. Further KPP simulations demonstrate that the MJO in SPCCSM3 arises from an overly strong sensitivity to El Niño–Southern Oscillation events. Our results show that simulated interannual variability and coupled‐model mean state biases affect the perceived response of the MJO to coupling. This is particularly concerning in the context of internal variability in coupled models, as many previous MJO sensitivity studies in coupled models used relatively short (20‐ to 50‐year) simulations that undersample interannual‐decadal variability. Diagnosing the effects of coupling on the MJO requires simulations that carefully control for mean state biases and interannual variability.

Interannual variability of summer surface mass balance and surface melting in the Amundsen sector, West Antarctica

Abstract. Understanding the interannual variability of surface mass balance (SMB) and surface melting in Antarctica is key to quantify the signal-to-noise ratio in climate trends, identify opportunities for multi-year climate predictions and assess the ability of climate models to respond to climate variability. Here we simulate summer SMB and surface melting from 1979 to 2017 using the Regional Atmosphere Model (MAR) at 10 km resolution over the drainage basins of the Amundsen Sea glaciers in West Antarctica. Our simulations reproduce the mean present-day climate in terms of near-surface temperature (mean overestimation of 0.10 ∘C), near-surface wind speed (mean underestimation of 0.42 m s−1), and SMB (relative bias <20 % over Thwaites glacier). The simulated interannual variability of SMB and melting is also close to observation-based estimates. For all the Amundsen glacial drainage basins, the interannual variability of summer SMB and surface melting is driven by two distinct mechanisms: high summer SMB tends to occur when the Amundsen Sea Low (ASL) is shifted southward and westward, while high summer melt rates tend to occur when ASL is shallower (i.e. anticyclonic anomaly). Both mechanisms create a northerly flow anomaly that increases moisture convergence and cloud cover over the Amundsen Sea and therefore favors snowfall and downward longwave radiation over the ice sheet. The part of interannual summer SMB variance explained by the ASL longitudinal migrations increases westward and reaches 40 % for Getz. Interannual variation in the ASL relative central pressure is the largest driver of melt rate variability, with 11 % to 21 % of explained variance (increasing westward). While high summer SMB and melt rates are both favored by positive phases of El Niño–Southern Oscillation (ENSO), the Southern Oscillation Index (SOI) only explains 5 % to 16 % of SMB or melt rate interannual variance in our simulations, with moderate statistical significance. However, the part explained by SOI in the previous austral winter is greater, suggesting that at least a part of the ENSO–SMB and ENSO–melt relationships in summer is inherited from the previous austral winter. Possible mechanisms involve sea ice advection from the Ross Sea and intrusions of circumpolar deep water combined with melt-induced ocean overturning circulation in ice shelf cavities. Finally, we do not find any correlation with the Southern Annular Mode (SAM) in summer.

Modeling the Influence of the Weddell Polynya on the Filchner–Ronne Ice Shelf Cavity

ABSTRACT Open-ocean polynyas in the Weddell Sea of Antarctica are the product of deep convection, which transports Warm Deep Water (WDW) to the surface and melts sea ice or prevents its formation. These polynyas occur only rarely in the observational record but are a near-permanent feature of many climate and ocean simulations. A question not previously considered is the degree to which the Weddell polynya affects the nearby Filchner–Ronne Ice Shelf (FRIS) cavity. Here we assess these effects using regional ocean model simulations of the Weddell Sea and FRIS, where deep convection is imposed with varying area, location, and duration. In these simulations, the idealized Weddell polynyas consistently cause an increase in WDW transport onto the continental shelf as a result of density changes above the shelf break. This leads to saltier, denser source waters for the FRIS cavity, which then experiences stronger circulation and increased ice shelf basal melting. It takes approximately 14 years for melt rates to return to normal after the deep convection ceases. Weddell polynyas similar to those seen in observations have a modest impact on FRIS melt rates, which is within the range of simulated interannual variability. However, polynyas that are larger or closer to the shelf break, such as those seen in many ocean models, trigger a stronger response. These results suggest that ocean models with excessive Weddell Sea convection may not be suitable boundary conditions for regional models of the Antarctic continental shelf and ice shelf cavities.

Evaluation of FAMIL2 in Simulating the Climatology and Seasonal‐to‐Interannual Variability of Tropical Cyclone Characteristics

AbstractWe evaluate the ability of the latest generation atmospheric general circulation model from State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (namely, FAMIL2) in simulating some key characteristics (genesis location, track, number, and intensity) of tropical cyclones (TCs) in terms of their climatology and seasonal to interannual variability. A standard 1° × 1° atmospheric model intercomparison project experiment is carried out for the period 1979–2002, and the last 20 years of outputs are used for analysis. The same period from International Best Track Archive for Climate Stewardship (IBTrACS) is used as the observation for comparison purposes. The evaluations focus on TC activity at the global scale as well as in the three key regions of the northern Indian Ocean (NIO), western Pacific (WP) and northern Atlantic (NA). With respect to the simulated TC climatology, FAMIL2 shows notable ability in correctly reproducing the main characteristics of the genesis locations, tracks, and numbers of TC, particularly over the key regions of TC activity in the Northern Hemisphere; whereas, it underestimates the intensities of TC, as is the case with many state‐of‐the‐art climate models operating at a medium resolution. On seasonal‐to‐interannual time scales, meanwhile, FAMIL2 successfully reproduces the seasonal cycles of TC numbers over the NIO and WP regions, the former being characterized by double TC peaks (in May and October) and the latter by a maximum peak season in August. However, the model only captures these features approximately. For the simulated interannual variability of TC activity, the correlation coefficients of 20‐year TC numbers between FAMIL2 and IBTrACS are 0.22, 0.51 (95% confidence interval), and 0.49 (95% confidence interval) for the NIO, WP, and NA, respectively. We also examine the possible reasons behind the performance of FAMIL2 by investigating its subseasonal signs related to the Madden‐Julian Oscillation (MJO) and convectively coupled equatorial waves. The TC genesis potential index is employed to investigate the possible impacts of the large‐scale dynamic fields on the simulation of TC activity. Finally, the biases of simulated TC activity, as well as possible solutions for these biases, are discussed with respect to the horizontal resolution of the model. A TC forecasting case study is introduced as a first step in applying FAMIL2 to a TC forecasting system.

Empirical Run‐Time Bias Correction for Antarctic Regional Climate Projections With a Stretched‐Grid AGCM

AbstractThis work presents snapshot simulations of the late 20th and late 21st century Antarctic climate under the RCP8.5 scenario carried out with an empirically bias‐corrected global atmospheric general circulation model (AGCM), forced with bias‐corrected sea‐surface temperatures and sea ice and run with about 100‐km resolution over Antarctica. The bias correction substantially improves the simulated mean late 20th century climate. The simulated atmospheric circulation of the bias‐corrected model compares very favorably to the best available AMIP (Atmospheric Model Intercomparison Project)‐type climate models. The simulated interannual circulation variability is improved by the bias correction. Depending on the metric, a slight improvement or degradation is found in the simulated variability on synoptic timescales. The simulated climate change over the 21st century is broadly similar in the corrected and uncorrected versions of the atmospheric model, and atmospheric circulation patterns are not geographically “pinned” by the applied bias correction. These results suggest that the method presented here can be used for bias‐corrected climate projections. Finally, the authors discuss different possible choices in terms of the place of bias corrections and other intermediate steps in the modeling chain leading from global coupled climate simulations to impact assessment.

Subsurface ocean biases in climate models and its implications in the simulated interannual variability: A case study for Indian Ocean

Coupled ocean atmosphere general circulation models (CGCMs) are known to have deep ocean biases when simulated over long term climate timescales. Here, an analysis has been done for investigating the impacts of these deep ocean biases, especially those which occur in temperature and salinity, ensuing biases in ocean dynamics and large scale air-sea interactions in state of the art CGCMs. The outputs from historical runs of 20 Coupled Model Intercomparison Project Phase 5 (CMIP-5) models have been analyzed for Indian Ocean. All candidate models develop internal warm and saline biases approximately between a depth range of 100 and 800 m in long term simulations. These internal biases are found to have implications in large scale ocean dynamics via their linkage through baroclinicity of the ocean. The role of internal biases in the ensuing dynamics is correlated via relations between Brunt Väisälä frequency (N2) and baroclinic wave speeds using both Sturm-Loiuville theorem and WKBJ approximation. The CMIP-5 models analyzed here have a higher baroclinic speed compared to that of observations. Annual propagating modes in the ocean reveal higher speeds in most of the models, and that the phase reversal is taking place at lead or lag by a month or more as compared to observations. The study suggests that faster wave propagation in climate models due to subsurface biases in temperature, salinity, N2 and baroclinicity have potential to impact simulated planetary scale events in terms of their life cycle, periodicity and seasonality as analyzed with Indian Ocean Dipole Zonal Mode. A corollary being a cautionary outlook on climate projections made by coupled models as long as the biases are persistent.

The influence of single model ensemble on the simulated extratropical interannual variability

The influence of single model ensemble on the simulated extratropical interannual variability

Biogeochemical modelling of dissolved oxygen in a changing ocean.

Secular decreases in dissolved oxygen concentration have been observed within the tropical oxygen minimum zones (OMZs) and at mid- to high latitudes over the last approximately 50 years. Earth system model projections indicate that a reduction in the oxygen inventory of the global ocean, termed ocean deoxygenation, is a likely consequence of on-going anthropogenic warming. Current models are, however, unable to consistently reproduce the observed trends and variability of recent decades, particularly within the established tropical OMZs. Here, we conduct a series of targeted hindcast model simulations using a state-of-the-art global ocean biogeochemistry model in order to explore and review biases in model distributions of oceanic oxygen. We show that the largest magnitude of uncertainty is entrained into ocean oxygen response patterns due to model parametrization of pCO2-sensitive C : N ratios in carbon fixation and imposed atmospheric forcing data. Inclusion of a pCO2-sensitive C : N ratio drives historical oxygen depletion within the ocean interior due to increased organic carbon export and subsequent remineralization. Atmospheric forcing is shown to influence simulated interannual variability in ocean oxygen, particularly due to differences in imposed variability of wind stress and heat fluxes.This article is part of the themed issue 'Ocean ventilation and deoxygenation in a warming world'.

Maize Yield as a Function of Water Availability across Precipitation Years in the North China Plain

Crop water production functions require precise knowledge of crop productivity across a wide range of soil water availability (WA). Field studies have demonstrated the yield response of maize (Zea mays L.) to WA, but the relationship has not been quantified across various precipitation years in the North China Plain (NCP). This study was conducted to investigate the effects of the simulated interannual variability in precipitation on maize yield in a winter wheat (Triticum aestivum L.) –summer maize double‐cropping system in the NCP, in 2014 and 2015. A rainfall simulator system was used to simulate contrasting precipitation years, including wet, normal, normal‐dry, and dry years. A fixed amount of 70 mm irrigation was applied at different growth stages of maize, including at planting (I70), at planting and tasseling (VT) stages (I140), and at planting, VT, and milk stages (I210). The objective was to quantify the relationships among crop production, water use, and WA for maize. We found that I210 improved soil water storage (SWS, mm) by 19 to 36% in the midseason of normal‐dry and dry years but had no effect in normal and wet years, compared with I70. Crop evapotranspiration (ETc, mm d−1) was least in the dry year with the I70 treatment and differed with maize growth phases, with the peak occurring midseason for maize in both years. Regression analyses showed that ETc was negatively related to SWS but positively related to WA. A quadratic function described grain yield responses to WA and ETc, with total WA of 478 mm and daily mean ETc of 5.1 mm d−1 producing greatest grain yield. With yield response function acquired, we conclude that target maximum yield of 11.6 Mg ha−1 can be achieved after probability of exceedance of precipitation equals 18 with irrigation water requirement applied.

Getting the right wind-forcing for an ecosystem model: A case study from the eastern Arabian Sea

ABSTRACTThe state of the ocean simulated through a numerical model is extremely sensitive to the forcing and hence, the choice of appropriate forcing is essential, in particular wind-fields. We compared Regional Ocean Modeling System (ROMS) simulations when forced with two different wind-fields derived from satellite sensors (scatterometers), namely QuickSCAT and ASCAT, while keeping the remaining configurations the same. This study had been carried out during the operational overlapping period of these scatterometers (2007–2009). We observed that the simulations of upper ocean chlorophyll distribution and depth of sub-surface chlorophyll maximum were vastly diverging when the model was forced with different scatterometer forcings. In order to determine which of the forcings yielded coastal productivity closer to observations, we compared model-simulated upper ocean chlorophyll concentration with in situ observations along the eastern Arabian Sea. For all the sub-regions, the QuickSCAT wind-forced coastal productivity was better correlated with in situ chlorophyll than its counterpart. However, this signature was most prominent in the wind-driven upwelling regime of the south-eastern Arabian Sea. Additionally, when compared with surface chlorophyll derived from SeaWiFS and MODIS-Aqua satellites, we found that the QuickSCAT simulated interannual variability was in better agreement in comparison to its counterpart.

Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations.

Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.

Improved simulation of the East Asian winter monsoon interannual variation by IAP/LASG AGCMs

AbstractThe simulation of the East Asian winter monsoon (EAWM) has been a challenge for climate models. In this study, the performances of two versions of the AGCM developed at the IAP, versions 1 and 2 of the Grid-point Atmospheric Model of the IAP/LASG (GAMIL1 and GAMIL2), are evaluated in the context of mean state and interannual variation. Significant improvements are shown for GAMIL2 in comparison to GAMIL1. The simulated interannual variability of the EAWM, measured by the regional average of 1000 hPa meridional wind over East Asia, has evidently improved; the correlation coefficient with reanalysis data changes from 0.37 in GAMIL1 to 0.71 in GAMIL2. The associated interannual precipitation anomalies are also improved, in terms of both spatial pattern and magnitude. Analysis demonstrates that the improvements result from the better simulation of the El Nino-related Philippine Sea anticyclone (PSAC) in GAMIL2. The improved moist processes, including the stratiform condensation and evaporation in GAMI...

Multi-model ensemble analysis of Pacific and Atlantic SST variability in unperturbed climate simulations

We assess internally-generated climate variability expressed by a multi-model ensemble of unperturbed climate simulations. We focus on basin-scale annual-average sea surface temperatures (SSTs) from twenty multicentennial pre-industrial control simulations contributing to the fifth phase of the Coupled Model Intercomparison Project. Ensemble spatial patterns of regional modes of variability and ensemble (cross-)wavelet-based phase-frequency diagrams of corresponding paired indices summarize the ensemble characteristics of inter-basin and regional-to-global SST interactions on a broad range of timescales. Results reveal that tropical and North Pacific SSTs are a source of simulated interannual global SST variability. The North Atlantic-average SST fluctuates in rough co-phase with the global-average SST on multidecadal timescales, which makes it difficult to discern the Atlantic Multidecadal Variability (AMV) signal from the global signal. The two leading modes of tropical and North Pacific SST variability converge towards co-phase in the multi-model ensemble, indicating that the Pacific Decadal Oscillation (PDO) results from a combination of tropical and extra-tropical processes. No robust inter- or multi-decadal inter-basin SST interaction arises from our ensemble analysis between the Pacific and Atlantic oceans, though specific phase-locked fluctuations occur between Pacific and Atlantic modes of SST variability in individual simulations and/or periods within individual simulations. The multidecadal modulation of PDO by the AMV identified in observations appears to be a recurrent but not typical feature of ensemble-simulated internal variability. Understanding the mechanism(s) and circumstances favoring such inter-basin SST phasing and related uncertainties in their simulated representation could help constraining uncertainty in decadal climate predictions.

Seasonal and spatial variations of global aerosol optical depth: multi-year modelling with GEOS-Chem-APM and comparisons with multiple-platform observations

Recent AeroCom phase II experiments showed a large diversity in aerosol optical depth (AOD) among 16 detailed global aerosol models, which contributes to the large uncertainty in the predicted aerosol radiative forcing. The GEOS-Chem-APM, a global size-resolved aerosol model, can be considered as a representative AeroCom II model. In this study, multi-year AOD data (2004–2012) from ground-based Aerosol Robotic Network (AERONET) measurements and Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite retrievals are used to evaluate the performance of GEOS-Chem-APM in capturing observed seasonal and spatial AOD variations. Compared to the observations, the modelled AOD is overall good over land, but quite low over ocean possibly due to low sea salt emission in the model and/or higher AOD in satellite retrievals, specifically MODIS and MISR. We chose 72 AERONET sites having at least 36 months data available and representative of high spatial domain to compare with the model and satellite data. Comparisons in various representative regions show that the model overall agrees well in the major anthropogenic emission regions, such as Europe, East Asia and North America. Relative to the observations, the modelled AOD is systematically lower in biomass burning regions such as South Africa and South America possibly due to uncertainties in emission inventory, but slightly higher in North Africa likely associated with stronger dust emissions in the model. The model is able to capture the realistic seasonal cycle in all regions, including the peak of AOD in major dust events months and biomass burning seasons. The simulated inter-annual variability is overall consistent with the observations, which is distinctly shown in South Africa and South America with strong inter-annual variability compared to other regions.