Surface Air Temperature Fields Research Articles

Can climate change signals be detected from the terrestrial water storage at daily timescale?

The global terrestrial water storage (TWS), the most accessible component in the hydrological cycle, is a general indicator of freshwater availability on Earth. The global TWS trend caused by climate change is harder to detect than global mean temperature due to the highly uneven hydrological responses across the globe, the brevity of global freshwater observations, and large noises of internal climate variability. To overcome the climate noise and small sample size of observations, we leverage the vast amount of observed and simulated meteorological fields at daily scales to project global TWS through its fingerprints in weather patterns. The novel method identifies the relationship between annual global mean TWS and daily surface air temperature and humidity fields using multi-model hydrological simulations. We found that globally, approximately 50% of days for most years since 2016 have climate change signals emerged above the noise of internal variability. Climate change signals in global mean TWS have been consistently increasing over the last few decades, and in the future, are expected to emerge from the natural climate variability. Our research indicates the urgency to limit carbon emission to not only avoid risks associated with warming but also sustain water security in the future.

Влияние метеофакторов на среднесуточную интенсивность городского острова тепла Екатеринбурга

Urban heat island (UHI) intensity in the surface air temperature field is equal to the difference between temperatures (averaged for the period under consideration) in the city and in the immediate rural surroundings. Using multi-regression analysis, We investigated the meteorological factors determining the mean daily UHI intensity based on observational data at weather stations in Yekaterinburg and the village of Verkhneye Dubrovo in 2020–2022. The average value of an UHI intensity in May-September equals to 0.94 °C with a standard deviation of daily mean values of 0.71 °C. The multiple linear regression model includes the difference in the relative humidity of the atmosphere in the city and the village, the weather factor determined by wind speed and cloudiness, and atmospheric pressure. This “summer” model describes 60 % of the observed UHI variance. In December-March, the average UHI intensity equals to 0.81 °C with a standard deviation of mean daily values of 0.82 °C. The regression model includes only the weather factor explaining 27 % of the UHI variability. It is concluded that the UHI intensity in Yekaterinburg is primarily determined by solar radiation and various properties of urban and rural surfaces (albedo, emissivity, thermal properties of soils). They determine the maximal UHI intensity that is realized only in “ideal” weather (weather factor ~1). Deviations from “ideal” conditions described by meteorological factors only reduce the maximal UHI intensity. In winter, heat leaks from buildings and heating networks weakly dependent on the meteorological factors make an additional contribution to the UHI intensity.

Modulation by the QBO of the Relationship between the NAO and Northeast China Temperature in Late Winter

Abstract The North Atlantic Oscillation (NAO) generally has an in-phase relationship with surface air temperature (SAT) anomalies over Northeast China in late winter. The present study shows that such an NAO–SAT relationship becomes stronger during easterly phases of the quasi-biennial oscillation (QBO), but is relatively weak during westerly phases. Observational evidence reveals that the modulation effect of the QBO on the NAO–SAT relationship over Northeast China is attributable to QBO-induced changes in the spatial structure of the NAO and associated stratosphere–troposphere coupling. During easterly QBO phases, the NAO has a strong connection with the Northern Hemisphere stratospheric polar vortex, facilitating a hemisphere-wide structure of the NAO and thus a downstream extension of NAO signal from the Euro–Atlantic sector toward Northeast Asia. During westerly QBO phases, however, the NAO has a limited connection with the stratospheric polar vortex. In this case, the NAO features a classically regional mode, with the signal in the SAT field mainly confined to the Euro-Atlantic sector. By examining historical simulations from six climate models participating in CMIP6 and including stratospheric processes, it is found that none of these models captures a significant difference in the spatial structure of the NAO and its connection with the stratospheric polar vortex between the easterly and westerly QBO phases in late winter. A key reason may be related to the poor performance of the models in simulating the Holton–Tan effect, which is critical for linking the QBO and NAO. Significance Statement The QBO is the dominant mode of equatorial stratospheric variability and important for seasonal forecasting. This study aims to better understand the surface influence of the QBO by examining the relationship between the NAO and temperature anomalies over China according to the phases of the QBO during boreal winter. Our results highlight the importance of the QBO in modulating the spatial structure and thus climate impact of the NAO via stratosphere–troposphere coupling in observations, while state-of-the-art climate models perform poorly in simulating the QBO-related stratosphere–troposphere coupling and thus the QBO–NAO connection. These findings have important implications for seasonal prediction and model development in the future.

A Hierarchy of Global Ocean Models Coupled to CESM1

AbstractWe develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed‐layer model (MLM) with entrainment and detrainment, an Ekman MLM, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting three‐dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed‐layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid‐latitude westerly jet and of the Inter‐tropical Convergence Zone (ITCZ) in response to sea‐ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed‐layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.

A Dynamical Systems Perspective to Characterize the El Niño Diversity in Spatiotemporal Patterns

El Niño is the long-lasting anomalous warming of sea surface temperature (SST) and surface air temperature (SAT) over the tropical Pacific. Each El Niño event has its unique impact on the overlaying atmosphere, where the warming exhibits diversity in spatiotemporal patterns. It still remains an open question for discriminating the El Niño diversity, since the single area-averaging SST index often fails to distinguish the impact of the event diversity, which is partially due to the nonlinear and non-uniform variations of the warming patterns. Here, we introduced the Dynamical Systems metrics (DSMs) to measure instantaneous dimensions and persistence of the SAT warming patterns over the tropical Pacific. Our results show that different SAT warming patterns can be discriminated by their corresponding values of dimension and persistence, then the central Pacific and eastern Pacific El Niño events can be discriminated by DSM. Particularly, through the analyses of El Niño events, we can interpret the physical meaning of DSM parameters applied to the space-time SAT field: an instantaneous dimension reflects whether the sub-regions of the SAT field are consistently varying and to what degree the spatial pattern of anomalies is homogeneous, while the instantaneous persistence indicates how long an anomalous SAT pattern can be maintained. This work analyzes the spatiotemporal variability of El Niño from a dynamical system perspective, and DSM may also serve as a useful tool to study extreme events related to SST anomalies.

The Impact of the Indian Ocean Basin Mode on Antarctic Sea Ice Concentration in Interannual Time Scales

AbstractThe Antarctic sea ice variability has been linked to tropical sea surface temperature. However, little is known as to whether and how the Indian Ocean Basin Mode (IOBM) influences Antarctic sea ice changes. We revealed the existence of a teleconnection between the IOMB and Antarctic sea ice anomalies, which is much stronger in austral spring and autumn than summer and winter. In particular, under the positive phase of the IOBM, significant positive sea ice anomalies occur in the Bellingshausen and northern Weddell Seas, in contrast to negative anomalies in the Amundsen Sea, the southern Atlantic Ocean, and the coastal seas off Dronning Maud Land. This teleconnection is established by planetary wavetrains excited over the tropical Indian Ocean and the tropical Pacific Ocean and is modulated by El Niño‐Southern Oscillation. The IOBM‐related Antarctic sea ice anomalies are largely consistent with those of the anomalous surface air temperature and wind fields associated with the IOBM.

Dynamical Downscaling of Surface Air Temperature and Wind Field Variabilities over the Southeastern Levantine Basin, Mediterranean Sea

The characteristics of near surface air temperature and wind field over the Southeastern Levantine (SEL) sub-basin during the period 1979–2018 were simulated. The simulation was carried out using a dynamical downscaling approach, which requires running a regional climate model system (RegCM-SVN6994) on the study domain, using lower-resolution climate data (i.e., the fifth generation of ECMWF atmospheric reanalysis of the global climate ERA5 datasets) as boundary conditions. The quality of the RegCM-SVN simulation was first verified by comparing its simulations with ERA5 for the studied region from 1979 to 2018, and then with the available five WMO weather stations from 2007 to 2018. The dynamical downscaling results proved that RegCM-SVN in its current configuration successfully simulated the observed surface air temperature and wind field. Moreover, RegCM-SVN was proved to provide similar or even better accuracy (during extreme events) than ERA5 in simulating both surface air temperature and wind speed. The simulated annual mean T2m by RegCM-SVN (from 1979 to 2018) was 20.9 °C, with a positive warming trend of 0.44 °C/decade over the study area. Moreover, the annual mean wind speed by RegCM-SVN was 4.17 m/s, demonstrating an annual negative trend of wind speed over 92% of the study area. Surface air temperatures over SEL mostly occurred within the range of 4–31 °C; however, surface wind speed rarely exceeded 10 m/s. During the study period, the seasonal features of T2m showed a general warming trend along the four seasons and showed a wind speed decreasing trend during spring and summer. The results of the RegCM-SVN simulation constitute useful information that could be utilized to fully describe the study area in terms of other atmospheric parameters.

Weakened Antarctic Dipole Under Global Warming in CMIP6 Models

AbstractThe Antarctic dipole (ADP) depicts the leading mode of interannual variability over the Antarctic region in both sea ice and surface air temperature (SAT) fields. The ADP is superimposed on the long‐term climatic trends, modulating the response of the mean climate to external forcing by modifying the signal‐to‐noise ratio. The response of the ADP to greenhouse warming is unknown. Based on the Coupled Model Intercomparison Project Phase 6 models that capture main characteristics of the ADP, we find a robust reduction in variability of ADP under greenhouse warming. The warming‐induced sea ice loss limits the magnitude of ADP SAT variability through surface albedo feedback. The El Niño‐Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) are two main triggering mechanisms of the ADP on interannual time scale. In a warmer climate, decreased variability of the SAM contributes to weakened ADP but increased ENSO variability plays an offsetting role.

A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p = 0.04 ) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p = 0.05 ). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p = 0.01 ∼ 0.27 ) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p = 0.04 ∼ 0.42 ) with a peak over the higher latitudes.

Improvements in tropical precipitation and sea surface air temperature fields in a coupled atmosphere–ocean data assimilation system

AbstractA coupled atmosphere–ocean data assimilation system, the Meteorological Research Institute‐Coupled Data Assimilation System Version 1 (MRI‐CDA1), was developed based on the coupled atmosphere–ocean general circulation model and separate atmosphere and ocean analysis routines operated by the Japan Meteorological Agency (JMA). To implement coupled atmosphere–ocean data assimilation, 6‐hr data assimilation cycles with the coupled model in the outer loop are adopted in atmospheric data assimilation, whereas incremental analysis updates with 10‐day data assimilation cycles are adopted in ocean data assimilation. A coupled data assimilation (reanalysis) experiment (CDA‐Exp) is conducted using MRI‐CDA1 along with an uncoupled reanalysis experiment (UCPL‐Exp) in which the same atmospheric component of the coupled model is forced by prescribed sea surface temperature (SST) similarly to the JMA reanalysis, JRA‐55. Climatological precipitation and variations in precipitation and sea surface air temperature (SAT) are better represented in CDA‐Exp than in JRA‐55, which is brought about by the modification of the atmosphere model physics of the coupled model to better represent climate states. In CDA‐Exp, the SST adjustment to the atmosphere amplifies the lead/lag correlations between subseasonal variations of SST and precipitation. The atmosphere–ocean coupling generates SST variations associated with tropical instability waves, and the SAT field responds to SST variations. The SST–precipitation and SST–SAT relationships on the weather timescale are also recovered in CDA‐Exp, although they are hardly seen in UCPL‐Exp. The coupled model physics generates weather‐timescale SST variations consistent with the atmospheric state, and the atmospheric parameters respond to the SST variations through the coupled model physics. This study suggests that the benefits of coupled data assimilation would be more evident if the ability of MRI‐CDA1 to represent SST variations and its interaction with the atmosphere are improved.

Analysis of 20th century surface air temperature using linear dynamical modes.

A Bayesian Linear Dynamical Mode (LDM) decomposition method is applied to isolate robust modes of climate variability in the observed surface air temperature (SAT) field. This decomposition finds the optimal number of internal modes characterized by their own time scales, which enter the cost function through a specific choice of prior probabilities. The forced climate response, with time dependence estimated from state-of-the-art climate-model simulations, is also incorporated in the present LDM decomposition and shown to increase its optimality from a Bayesian standpoint. On top of the forced signal, the decomposition identifies five distinct LDMs of internal climate variability. The first three modes exhibit multidecadal scales, while the remaining two modes are attributable to interannual-to-decadal variability associated with El Niño-Southern oscillation; all of these modes contribute to the secular climate signal-the so-called global stadium wave-missing in the climate-model simulations. One of the multidecadal LDMs is associated with Atlantic multidecadal oscillation. The two remaining slow modes have secular time scales and patterns exhibiting regional-to-global similarities to the forced-signal pattern. These patterns have a global scale and contribute significantly to SAT variability over the Southern and Pacific Oceans. In combination with low-frequency modulation of the fast LDMs, they explain the vast majority of the variability associated with interdecadal Pacific oscillation. The global teleconnectivity of the secular climate modes and their possible crucial role in shaping the forced climate response are the two key dynamical questions brought about by the present analysis.

Northern Hemisphere Winter Air Temperature Patterns and Their Associated Atmospheric and Ocean Conditions

AbstractThe Northern Hemisphere (NH) has experienced winter Arctic warming and continental cooling in recent decades, but the dominant patterns in winter surface air temperature (SAT) are not well understood. Here, a self-organizing map (SOM) analysis is performed to identify the leading patterns in winter daily SAT fields from 1979 to 2018, and their associated atmospheric and ocean conditions are also examined. Three distinct winter SAT patterns with two phases of nearly opposite signs and a time scale of 7–12 days are found: one pattern exhibits concurrent SAT anomalies of the same sign over North America (NA) and northern Eurasia, while the other two patterns show SAT anomalies of opposite signs between, respectively, NA and the Bering Sea, and the Kara Sea and East Asia (EA). Winter SAT variations may arise from changes in the SOM frequencies. Specifically, the observed increasing trends of winter cold extremes over NA, central Eurasia, and EA during 1998–2013 can be understood as a result of the increasing occurrences of some specific SAT patterns. These SOMs are closely related to poleward advection of midlatitude warm air and equatorward movements of polar cold airmass. These meridional displacements of cold and warm airmasses cause concurrent anomalies over different regions not only in SAT but also in water vapor and surface downward longwave radiation. Anomalous sea surface temperatures in the tropical Pacific, midlatitude North Pacific, and North Atlantic and anomalous Arctic sea ice concentrations also concur to support and maintain the anomalous atmospheric circulation that causes the SAT anomalies.

Correcting citizen‐science air temperature measurements across the Netherlands for short wave radiation bias

AbstractCitizen‐science thermometer measurements have the potential to provide information about surface air temperature fields on scales smaller than is typically quantified by the official monitoring network. As such, national meteorological services are becoming increasingly interested in these measurements as a possible source of data for use in weather monitoring or forecasting. However, in order for the information to be used, biases in the data need to be assessed. The most important source of bias is the potential overheating of the thermometer due to inadequate shielding or exposure. Previous research has indicated that information about the nature of the instrument and its exposure is important for correcting this bias. However, in the majority of cases this information is unavailable for amateur stations. In this paper a statistical correction for short wave radiation bias is developed for the air temperature data recorded at 159 Weather Observations Website (WOW) stations across the Netherlands during the period 2015–2016. Generalized additive mixed modelling (GAMM) is used to quantify and correct for short wave radiation bias in the hourly measurements using a background temperature field generated from the official 34 automatic weather stations along with satellite‐derived short wave radiation estimates. It is demonstrated that the corrected WOW data add local detail to the hourly temperature field, which may provide a useful source of data to supplement official measurements.

Climate network percolation reveals the expansion and weakening of the tropical component under global warming

Global climate warming poses a significant challenge to humanity; it is associated with, e.g., rising sea level and declining Arctic sea ice. Increasing extreme events are also considered to be a result of climate warming, and they may have widespread and diverse effects on health, agriculture, economics, and political conflicts. Still, the detection and quantification of climate change, both in observations and climate models, constitute a main focus of the scientific community. Here, we develop an approach based on network and percolation frameworks to study the impacts of climate changes in the past decades using historical models and reanalysis records, and we analyze the expected upcoming impacts using various future global warming scenarios. We find an abrupt transition during the evolution of the climate network, indicating a consistent poleward expansion of the largest cluster that corresponds to the tropical area, as well as the weakening of the strength of links in the tropic. This is found both in the reanalysis data and in the Coupled Model Intercomparison Project Phase 5 (CMIP5) 21st century climate change simulations. The analysis is based on high-resolution surface (2 m) air temperature field records. We discuss the underlying mechanism for the observed expansion of the tropical cluster and associate it with changes in atmospheric circulation represented by the weakening and expansion of the Hadley cell. Our framework can also be useful for forecasting the extent of the tropical cluster to detect its influence on different areas in response to global warming.

High-resolution dataset of urban canopy parameters for Beijing and its application to the integrated WRF/Urban modelling system

The Weather Research and Forecasting (WRF) model with urban canopy model has been widely used to study atmospheric boundary layer processes and urbanization-related environmental issues. One of the most daunting challenges faced by the integrated WRF/urban modelling system is constraining the uncertainties in urban canopy parameters (UCPs) that represent the urban morphological characteristics. In this paper, we develop a dataset of three-dimensional (3-D) UCPs for Beijing by using vector-format building information in a Geographic Information System (GIS) environment and apply it to the integrated WRF/urban modelling system to investigate the effects of UCPs on the simulated meteorological variables against observations in clear-sky days. Moreover, the role of spatially-varying UCPs involved in the Building Effect Parameterization (BEP) in capturing the heterogeneous characteristics of 2-m surface air temperature and 10-m wind field in the urban areas are evaluated. In general, high-resolution dataset of UCPs improves the model skill in simulating the diurnal variations and spatial distributions of 2-m surface air temperature and 10-m wind speed in the urban canopy, especially for the BEP. WRF/BEP with gridded UCPs dataset shows a better performance in densely built-up areas, where the morphological and physical characteristics of urban surface are complex. It also performs better in capturing the characteristics of urban heat islands (UHIs) and weak wind zones (WWZs) in urban areas. Results show that introducing the 3-D UCPs dataset into the integrated WRF/urban modelling system is a simple and yet efficient way to enhance the modelling capabilities in urban areas and assist urban planners to deal with urban environmental issues.

Greenland‐Wide Seasonal Temperatures During the Last Deglaciation

AbstractThe sensitivity of the Greenland ice sheet to climate forcing is of key importance in assessing its contribution to past and future sea level rise. Surface mass loss occurs during summer, and accounting for temperature seasonality is critical in simulating ice sheet evolution and in interpreting glacial landforms and chronologies. Ice core records constrain the timing and magnitude of climate change but are largely limited to annual mean estimates from the ice sheet interior. Here we merge ice core reconstructions with transient climate model simulations to generate Greenland‐wide and seasonally resolved surface air temperature fields during the last deglaciation. Greenland summer temperatures peak in the early Holocene, consistent with records of ice core melt layers. We perform deglacial Greenland ice sheet model simulations to demonstrate that accounting for realistic temperature seasonality decreases simulated glacial ice volume, expedites the deglacial margin retreat, mutes the impact of abrupt climate warming, and gives rise to a clear Holocene ice volume minimum.

Simulation of African dust properties and radiative effects during the 2015 SHADOW campaign in Senegal

The aim of this work is to estimate optical and radiative properties of dust aerosols and their potential feedbacks on atmospheric properties over Western Africa for the period 20 March–28 April 2015, by using numerical simulations and different sets of remote-sensing and in-situ measurements. Comparisons of simulations made by the on-line coupled meteorological-chemistry model WRF-CHEM with MODIS, AERONET and in-situ observations result in a general agreement for the spatio-temporal variations of aerosol extinction at both local and regional scales. Simulated SSA reached elevated values between 0.88 and 0.96 along the visible/near-infrared in close agreement with AERONET inversions, suggesting the predominance of dust over Western Africa during this specific period. This predominance of dust is confirmed by in-situ measurements of the aerosol size distribution, fitting well with the aerosols size distribution simulated by WRF-CHEM. The impact of this large dust load on the radiative fluxes leads to large modifications of the shortwave and longwave radiative budget both at the ground and at the top of the atmosphere. In return, the response of the atmosphere to these dust-induced radiative changes is the alteration of the surface air temperature and wind fields, with non-negligible impact on the dust emission and transport.

Using EOF Analysis over a Large Area for Assessing the Climate Impact of Small-Scale Afforestation in a Semiarid Region

AbstractThe authors suggest an approach to analyze the effects of small-scale afforestation on the surrounding climate of a large heterogenic area. While simple statistics have difficulty identifying the effect, here a well-known eigenvector technique is used to overcome several specific challenges that result from a limited research region, complex topography, and multiple atmospheric circulation patterns. This approach is applied to investigate the influence of the isolated Yatir forest, at the north edge of Israel’s Negev Desert. It was found that this forest does influence the daily climate, primarily seen in the main pattern of the empirical orthogonal function (EOF) of temperature and humidity. The EOF explains 93% and 80%, respectively, of the total variance in the data. Although the Yatir forest is small, it is significant in regulating the climate in the nearby surroundings, as it is located in a sharp transition area toward an arid climate. The results are presented as maps of correlation and regression between the normalized principal component time series of each pattern as well as other time series of the raw data and spatially interpolated data stations. Analysis of short-term campaign measurements around the Yatir forest supports the EOF results, and shows the forest’s influence to the south, mainly during nighttime when the forest becomes cooler than its surroundings. Overall, results suggest that in areas of transition to semiarid climates, forests regulate the surrounding surface air temperature and humidity fields. Wind analysis based on a complex EOF technique reveals the pattern of the daily cycle of surface wind over the region.

Building a traceable climate model hierarchy with multi-level emulators

Abstract. To study climate change on multi-millennial timescales or to explore a model's parameter space, efficient models with simplified and parameterised processes are required. However, the reduction in explicitly modelled processes can lead to underestimation of some atmospheric responses that are essential to the understanding of the climate system. While more complex general circulations are available and capable of simulating a more realistic climate, they are too computationally intensive for these purposes. In this work, we propose a multi-level Gaussian emulation technique to efficiently estimate the outputs of steady-state simulations of an expensive atmospheric model in response to changes in boundary forcing. The link between a computationally expensive atmospheric model, PLASIM (Planet Simulator), and a cheaper model, EMBM (energy–moisture balance model), is established through the common boundary condition specified by an ocean model, allowing for information to be propagated from one to the other. This technique allows PLASIM emulators to be built at a low cost. The method is first demonstrated by emulating a scalar summary quantity, the global mean surface air temperature. It is then employed to emulate the dimensionally reduced 2-D surface air temperature field. Even though the two atmospheric models chosen are structurally unrelated, Gaussian process emulators of PLASIM atmospheric variables are successfully constructed using EMBM as a fast approximation. With the extra information gained from the cheap model, the multi-level emulator of PLASIM's 2-D surface air temperature field is built using only one-third the amount of expensive data required by the normal single-level technique. The constructed emulator is shown to capture 93.2 % of the variance across the validation ensemble, with the averaged RMSE of 1.33 °C. Using the method proposed, quantities from PLASIM can be constructed and used to study the effects introduced by PLASIM's atmosphere.

Calculation of mass discharge of the Greenland ice sheet in the Earth System Model

Mass discharge calculation is a challenging task for the ice sheet modeling aimed at evaluation of their contribution to the global sea level rise during past interglacials, as well as one of the consequences of future climate change. In Greenland, ablation is the major source of fresh water runoff. It is approximately equal to the dynamical discharge (iceberg calving). Its share might have still larger during the past interglacials when the margins of the GrIS retreated inland. Refreezing of the melted water and its retention are two poorly known processes playing as a counterpart of melting and, thus, exerting influence on the run off. Interaction of ice sheets and climate is driven by energy and mass exchange processes and is complicated by numerous feed-backs. To study the complex of these processes, coupling of an ice sheet model and a climate model (i.e. models of the atmosphere and the ocean) in one model is required, which is often called the Earth System Model (ESM). Formalization of processes of interaction between the ice sheets and climate within the ESM requires elaboration of special techniques to deal with dramatic differences in spatial and temporal variability scales within each of three ESM’s blocks. In this paper, we focus on the method of coupling of a Greenland ice sheet model (GrISM) with the climate model INMCM having been developed in the Institute of Numerical Mathematics of Russian Academy of Sciences. Our coupling approach consists in applying of a special buffer model, which serves as an interface between GrISM and INMCM. A simple energy and water exchange model (EWBM-G) allows realistic description of surface air temperature and precipitation fields adjusted to a relief of elevation of the GrIS surface. In a series of diagnostic numerical experiments with the present-day GrIS geometry and the modeled climate we studied sensitivity of the modeled surface mass balance and run off to the key EWBM-G parameters and compared our results with similar model studies. In addition to the atmospheric, oceanic, and ice sheet blocks the ESM normally contains blocks accounting for dynamics of the biosphere, sea ice, hydrological cycle, etc. In practice, application of ESMs for research studies has become possible not long ago owing to the fast progress in computing facilities. Nevertheless, still now ESMs are rather computer time demanding. To provide long runs of a fully coupled ESM at a lower computational cost, we utilized an asynchronous coupling when a 100-yr run of the GrISM corresponds to 1-yr run of the INMCM. The weak point of the numerical experiments is comparison of the results with observations. The lack of observations in Greenland and significant inter-annual variability of air temperature, precipitation, surface melting, and run off do not allow formulation of a reliable reference «climate» and corresponding equilibrium state of GrIS. In practice it means that more or less accurate estimates of the past or future changes of the runoff and total GrIS mass discharge is reasonable to obtain in the form of deviations from a reference undisturbed model state.