Model Of Intermediate Complexity Research Articles

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Antarctic meltwater reduces the Atlantic meridional overturning circulation through oceanic freshwater transport and atmospheric teleconnections

The Atlantic meridional overturning circulation is an important component of the climate system because of its role in the heat transport. Its strength is sensitive to the surface density but mechanisms of the effect of Southern Ocean freshwater anomalies are relatively unknown. Here, we investigate the impact of Antarctic ice sheet meltwater on the Atlantic meridional overturning circulation using an earth system model of intermediate complexity. The meltwater over the Pacific sector of the Southern Ocean is transported to the east and, after passing the Drake Passage, travels northward reaching the North Atlantic. Furthermore, Southern Ocean cooling induces a northward shift of the Intertropical Convergence Zone, leading to more precipitation in the tropical Atlantic. Consequently, the reduced salinity weakens the Atlantic meridional overturning circulation. Additional experiments, in which the duration period of freshwater hosing was varied while keeping its total amount constant, indicate that the amplitude and the duration of the meltwater play crucial roles in determining the degree of reduction in Atlantic meridional overturning circulation.

For a Pluralism of Climate Modeling Strategies

Abstract The continued development of general circulation models (GCMs) toward increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channeling of research and funding to meet this aspiration. Yet many other modeling strategies have also been developed and can be used to understand past and present climates, to project future climates, and ultimately to support decision-making. We argue that a plurality of climate modeling strategies and an equitable distribution of funding among them would be an improvement on the current predominant strategy for informing policymaking. To support our claim, we use a philosophy of science approach to compare the increasing resolution and complexity of general circulation models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth system models of intermediate complexity. We show that each of these strategies prioritizes a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modeling strategies are all potentially useful and can be used in a complementary way.

Local summer temperature changes over the past 440 ka revealed by the total air content in the Antarctic EPICA Dome C ice core

Abstract. Seasonal temperature reconstructions from ice cores are missing over glacial–interglacial timescales, preventing a good understanding of the driving factors of Antarctic past climate changes. Here the total air content (TAC) record from the Antarctic EPICA Dome C (EDC) ice core is analyzed over the last 440 ka (thousand years). While the water isotopic record, a tracer for annual mean surface temperature, exhibits a dominant ∼100 kyr cyclicity, the TAC record is associated with a dominant ∼40 kyr cyclicity. Our results show that the TAC record is anti-correlated with the mean insolation over the local astronomical summer half-year. They also show for the first time that it is highly anti-correlated with local summer temperature simulated with an Earth system model of intermediate complexity. We propose that (1) the local summer insolation controls the local summer temperature; (2) the latter, through the development of temperature gradients at the near-surface of the ice sheet (<2 m), is affecting the surface snow structure; and (3) those snow structure changes propagating down to the bottom of the firn through densification are eventually controlling the pore volume at the bubble close-off and consequently the TAC. Hence, our results suggest that the EDC TAC record could be used as a proxy for local summer temperature changes. Also, our new simulations show that the mean insolation over the local astronomical summer half-year is the primary driver of Antarctic summer surface temperature variations, while changes in atmospheric greenhouse gas (GHG) concentrations and Northern Hemisphere (NH) ice sheet configurations play a more important role in Antarctic annual surface temperature changes.

Multiple thermal Atlantic Meridional Overturning Circulation thresholds in the intermediate complexity model Bern3D

Abstract. Variations in the Atlantic Meridional Overturning Circulation (AMOC) are associated with Northern Hemispheric and global climate shifts. Thermal thresholds of the AMOC have been found in a hierarchy of numerical circulation models, and there is an increasing body of evidence for the existence of highly sensitive AMOC modes where small perturbations can cause disproportionately large circulation and hence climatic changes. We discovered such thresholds in simulations with the intermediate-complexity Earth system model Bern3D, which is highly computationally efficient, allowing for studying this non-linear behaviour systematically over entire glacial cycles. By simulating the AMOC under different magnitudes of orbitally paced changes in radiative forcing over the last 788 000 years, we show that up to three thermal thresholds are crossed during glacial cycles in Bern3D and that thermal forcing could have destabilised the AMOC repeatedly. We present the circulation and sea ice patterns that characterise the stable circulation modes between which this model oscillates during a glacial cycle and assess how often and when thermal forcing could have preconditioned the Bern3D AMOC for abrupt shifts over the last 788 kyr.

Projecting Changes in the Frequency and Magnitude of Ozone Pollution Events Under Uncertain Climate Sensitivity

AbstractClimate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one‐way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate‐induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O3 episodes using extreme value theory, and extend the approach to projections of mid‐ and end‐century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O3 concentrations by over 2.3 ppb on average across the U.S. at mid‐century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non‐compliance due to a climate‐induced increase in frequency of high ozone days.

Open‐source stand‐alone version of atmospheric model Aeolus 2.0 software

AbstractIn this discourse, we present the unveiling of an open‐source software package designed to facilitate engagement with the atmospheric model, Aeolus 2.0. This particular iteration stands as a self‐contained model of intermediate complexity. The model's dynamical core is underpinned by a multi‐layer pseudo‐spectral moist‐convective Thermal Rotating Shallow Water (mcTRSW) model. The pseudo‐spectral problem‐solving tasks are handled by the Dedalus algorithm, acknowledged for its spin‐weighted spherical harmonics. The model captures the temporal and spatial evolution of vertically integrated potential temperature, thickness, water vapour, precipitation, and the intricate influence of bottom topography. It comprehensively characterizes velocity fields in both the lower and upper troposphere, employing resolutions spanning a spectrum from the smooth to the coarse, enabling the exploration of a wide range of dynamic phenomena with varying levels of detail and precision.

A Global Increase in Nearshore Tropical Cyclone Intensification

AbstractTropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979–2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24‐hr in regions close to the coast. Analysis of the observed large‐scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high‐resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification.

Diagnosing the causes of AMOC slowdown in a coupled model: a cautionary tale

Abstract. It is now established that the increase in atmospheric CO2 is likely to cause a weakening, or perhaps a collapse, of the Atlantic Meridional Overturning Circulation (AMOC). To investigate the mechanisms of this response in CMIP5 models, Levang and Schmitt (2020) have estimated the geostrophic streamfunction in these models offline and have decomposed the simulated changes into a contribution caused by the variations in temperature and salinity. They concluded that under a warming scenario, and for most models, the weakening of the AMOC is fundamentally driven by temperature anomalies, while freshwater forcing actually acts to stabilise it. However, given that both 3-D fields of ocean temperature and salinity are expected to respond to a forcing at the ocean surface, it is unclear to what extent the diagnostic is informative about the nature of the forcing. To clarify this question, we used the Earth system Model of Intermediate Complexity (EMIC), cGENIE, which is equipped with the C-GOLDSTEIN friction-geostrophic model. First, we reproduced the experiments simulating the Representative Concentration Pathway 8.5 (RCP8.5) warming scenario and observed that cGENIE behaves similarly to the majority of the CMIP5 models considered by Levang and Schmitt (2020), with the response dominated by the changes in the thermal structure of the ocean. Next, we considered hysteresis experiments associated with (1) water hosing and (2) CO2 increase and decrease. In all experiments, initial changes in the ocean streamfunction appear to be primarily caused by the changes in the temperature distribution, with variations in the 3-D distribution of salinity only partly compensating for the temperature contribution. These experiments also reveal limited sensitivity to changes in the ocean's salinity inventory. That the diagnostics behave similarly in CO2 and freshwater forcing scenarios suggests that the output of the diagnostic proposed in Levang and Schmitt (2020) is mainly determined by the internal structure of the ocean circulation rather than by the forcing applied to it. Our results illustrate the difficulty of inferring any information about the applied forcing from the thermal wind diagnostic and raise questions about the feasibility of designing a diagnostic or experiment that could identify which aspect of the forcing (thermal or haline) is driving the weakening of the AMOC.

Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)

Abstract. Stable water isotopes are used to infer changes in the hydrological cycle for different climate periods and various climatic archives. Following previous developments of δ18O in the coupled climate model of intermediate complexity, iLOVECLIM, we present here the implementation of the 1H2H16O and 1H217O water isotopes in the different components of this model and calculate the associated secondary markers deuterium excess (d-excess) and oxygen-17 excess (17O-excess) in the atmosphere and ocean. So far, the latter has only been modelled by the atmospheric model LMDZ4. Results of a 5000-year equilibrium simulation under preindustrial conditions are analysed and compared to observations and several isotope-enabled models for the atmosphere and ocean components. In the atmospheric component, the model correctly reproduces the first-order global distribution of the δ2H and d-excess as observed in the data (R=0.56 for δ2H and 0.36 for d-excess), even if local differences are observed. The model–data correlation is within the range of other water-isotope-enabled general circulation models. The main isotopic effects and the latitudinal gradient are properly modelled, similarly to previous water-isotope-enabled general circulation model simulations, despite a simplified atmospheric component in iLOVECLIM. One exception is observed in Antarctica where the model does not correctly estimate the water isotope composition, a consequence of the non-conservative behaviour of the advection scheme at a very low moisture content. The modelled 17O-excess presents a too-important dispersion of the values in comparison to the observations and is not correctly reproduced in the model, mainly because of the complex processes involved in the 17O-excess isotopic value. For the ocean, the model simulates an adequate isotopic ratio in comparison to the observations, except for local areas such as the surface of the Arabian Sea, a part of the Arctic and the western equatorial Indian Ocean. Data–model evaluation also presents a good match for the δ2H over the entire water column in the Atlantic Ocean, reflecting the influence of the different water masses.

SPEEDY-NEMO: performance and applications of a fully-coupled intermediate-complexity climate model

A fully-coupled general circulation model of intermediate complexity is documented. The study presents an overview of the model climatology and variability, with particular attention to the phenomenology of processes that are relevant for the predictability of the climate system on seasonal-to-decadal time-scales. It is shown that the model can realistically simulate the general circulation of the atmosphere and the ocean, as well as the major modes of climate variability on the examined time-scales: e.g. El Niño-Southern Oscillation, North Atlantic Oscillation, Tropical Atlantic Variability, Pacific Decadal Variability, Atlantic Multi-decadal Variability. Potential applications of the model are discussed, with emphasis on the possibility of generating sets of low-cost large-ensemble retrospective forecasts. We argue that the presented model is suitable to be employed in traditional and innovative model experiments that can play a significant role in future developments of seasonal-to-decadal climate prediction.

Shutdown of Atlantic overturning circulation could cause persistent increase of primary production in the Pacific

A potential shutdown of the Atlantic meridional overturning circulation (AMOC) is commonly recognized to have a significant impact on the Northern hemispheric climate, notably in Northern Europe. The collapse of the northbound heat transport by the AMOC is supposed to cool down surface air temperatures at the Scandinavian coast by up to 6 K accompanied by a concomitant nutrient starvation of phytoplankton in Subarctic and Arctic regions. However, besides local and regional impacts, tipping the AMOC into a weaker state by anthropogenic carbon dioxide (CO2) and associated freshwater forcing could also have surprising remote effects. In order to investigate possible long-term impacts of an AMOC shutdown on ocean biogeochemistry, we employ an Earth system model of intermediate complexity using idealized scenarios of century-scale atmospheric 2×CO2 and 4×CO2 pulses combined with North Atlantic freshwater forcing. The results show a continued increase in primary production, in particular in the Eastern equatorial Pacific, due to a decrease in iron limitation following the AMOC shutdown. Tracer simulations indicate that bioavailable dissolved iron brought by Aeolian dust into the subtropical gyres of the Atlantic Ocean is transported to the Southern Ocean and from there enters the Indian Ocean and the Pacific. Thereby, the additionally introduced iron fertilizes the phosphate-rich high-nutrient, low chlorophyll waters, giving a lasting boost to phytoplankton growth, especially in the Eastern equatorial Pacific.

Comparing machine-learning models of different levels of complexity for crop protection: A look into the complexity-accuracy tradeoff

Crop diseases and pests constitute significant causes of yield losses for crops. To limit the harm incurred by those events, farmers resort to plant protection products. Such products are known to have adverse effects both on the environment and on human health. Agronomists make continuous efforts to limit the usage of plant protection products to situations where those products are strictly necessary. To determine such situations, agronomists and policy-makers often rely on decision support tools to model and predict the dynamics of plant diseases. Decision support tools are based either on mechanistic models or on statistical approaches learned from large datasets of biotic (e.g., disease incidence, plant phenological stage) and abiotic (meteorological, soil characteristics) observations in cultures. The surge of powerful machine learning (ML) methods in the last decade makes such approaches a natural pathway to model the dynamics of plant diseases.Machine learning models can reveal the factors that contribute the most to disease and pests outbreaks, provided that those models are simple enough for human inspection. Simplicity, however, may come at the price of lower prediction performances when compared to more complex models.In this paper, we offer a deep look at the performance of ML models of different complexity when used on two use cases of crop disease prediction: downy mildew in the grapevine, and Cercospora leaf spot in the sugar beet.We compare model accuracy and complexity using a year-based cross-validation approach. Our results suggest that interannual meteorological variations are a very important factor in plant disease prediction. Moreover, in line with the observations of the research community in interpretable ML, model complexity stands in clear trade-off with accuracy. This makes models of intermediate complexity appealing for predicting the dynamics of crop diseases as they can provide explicit insights about the rationale of their predictions.

Synchronization phenomena observed in glacial–interglacial cycles simulated in an Earth system model of intermediate complexity

Abstract. The glacial–interglacial cycles of the Quaternary exhibit 41 kyr periodicity before the Mid-Pleistocene Transition (MPT) around 1.2–0.8 Myr ago and ∼ 100 kyr periodicity after that. From the viewpoint of dynamical systems, proposed mechanisms generating these periodicities are broadly divided into two types: (i) nonlinear forced responses of a mono- or multi-stable climate system to the astronomical forcing or (ii) synchronization of internal self-sustained oscillations to the astronomical forcing. In this study, we investigate the dynamics of glacial cycles simulated by the Earth system model of intermediate complexity CLIMBER-2 with a fully interactive carbon cycle, which reproduces the MPT under gradual changes in volcanic-CO2 degassing and regolith cover. We report that, in this model, the dominant frequency of glacial cycles is set in line with the principle of synchronization. It is found that the model exhibits self-sustained oscillations in the absence of astronomical forcing. Before the MPT, glacial cycles synchronize to the 41 kyr obliquity cycles because the self-sustained oscillations have periodicity relatively close to 41 kyr. After the MPT the timescale of internal oscillations becomes too long to follow every 41 kyr obliquity cycle, and the oscillations synchronize to the 100 kyr eccentricity cycles that modulate the amplitude of climatic precession. The latter synchronization occurs with the help of the 41 kyr obliquity forcing, which enables some terminations and glaciations to occur robustly at their right timing. We term this phenomenon vibration-enhanced synchronization because of its similarity to the noise-enhanced synchronization known in nonlinear science. While we interpret the dominant periodicities of glacial cycles as the result of synchronization of internal self-sustained oscillations to the astronomical forcing, the Quaternary glacial cycles show facets of both synchronization and forced response.

Rejuvenating the ocean: mean ocean radiocarbon, CO2 release, and radiocarbon budget closure across the last deglaciation

Abstract. Radiocarbon is a tracer that provides unique insights into the ocean's ability to sequester CO2 from the atmosphere. While spatial patterns of radiocarbon in the ocean interior can indicate the vectors and timescales for carbon transport through the ocean, estimates of the global average ocean–atmosphere radiocarbon age offset (B-Atm) place constraints on the closure of the global carbon cycle. Here, we apply a Bayesian interpolation method to compiled B-Atm data to generate global interpolated fields and mean ocean B-Atm estimates for a suite of time slices across the last deglaciation. The compiled data and interpolations confirm a stepwise and spatially heterogeneous “rejuvenation” of the ocean, suggesting that carbon was released to the atmosphere through two swings of a “ventilation seesaw” operating between the North Atlantic and both the Southern Ocean and the North Pacific. Sensitivity tests using the Bern3D model of intermediate complexity demonstrate that a portion of the reconstructed deglacial B-Atm changes may reflect “phase-attenuation” biases that are unrelated to ocean ventilation and that arise from independent atmospheric radiocarbon dynamics instead. A deglacial minimum in B-Atm offsets during the Bølling–Allerød could partly reflect such a bias. However, the sensitivity tests further demonstrate that when correcting for such biases, ocean “ventilation” could still account for at least one-third of deglacial atmospheric CO2 rise. This contribution to CO2 rise appears to have continued through the Younger Dryas, though much of the impact was likely achieved by the end of the Bølling–Allerød, indicating a key role for marine carbon cycle adjustment early in the deglacial process. Our global average B-Atm estimates place further new constraints on the long-standing mystery of global radiocarbon budget closure across the last deglaciation and suggest that glacial radiocarbon production levels are likely underestimated on average by existing reconstructions.

The Coupled Ice Sheet–Earth System Model Bern3D v3.0

Abstract Understanding climate variability from millennial to glacial–interglacial time scales remains challenging due to the complex and nonlinear feedbacks between ice, ocean, sediments, biosphere, and atmosphere. Complex climate models generally struggle to dynamically and comprehensively simulate such long time periods as a result of the large computational costs. Here, we therefore coupled a dynamical ice sheet model to the Bern3D Earth system model of intermediate complexity, which allows for simulating multiple glacial–interglacial cycles. The performance of the model is first validated against modern observations and its response to abrupt perturbations, such as atmospheric CO2 changes and North Atlantic freshwater hosing, is investigated. To further test the fully coupled model, the climate evolution over the entire last glacial cycle is explored in a transient simulation forced by variations in the orbital configuration and greenhouse gases and aerosols. The model simulates global mean surface temperature in fair agreement with reconstructions, exhibiting a gradual cooling trend since the last interglacial that is interrupted by two more rapid cooling events during the early Marine Isotope Stage (MIS) 4 and Last Glacial Maximum (LGM). Simulated Northern Hemispheric ice sheets show pronounced variability on orbital time scales, and ice volume more than doubles from MIS3 to the LGM in good agreement with recent sea level reconstructions. At the LGM, the Atlantic overturning has a strength of about 14 Sv (1 Sv ≡ 106 m3 s−1), which is a reduction by about one-quarter compared to the preindustrial. We thus demonstrate that the new coupled model is able to simulate large-scale aspects of glacial–interglacial cycles.

A transient coupled general circulation model (CGCM) simulation of the past 3 million years

Abstract. Driven primarily by variations in the earth's axis wobble, tilt, and orbit eccentricity, our planet experienced massive glacial/interglacial reorganizations of climate and atmospheric CO2 concentrations during the Pleistocene (2.58 million years ago (Ma)–11.7 thousand years ago (ka)). Even after decades of research, the underlying climate response mechanisms to these astronomical forcings have not been fully understood. To further quantify the sensitivity of the earth system to orbital-scale forcings, we conducted an unprecedented quasi-continuous coupled general climate model simulation with the Community Earth System Model version 1.2 (CESM1.2, ∼3.75∘ horizontal resolution), which covers the climatic history of the past 3 million years (3 Myr). In addition to the astronomical insolation changes, CESM1.2 is forced by estimates of CO2 and ice-sheet topography which were obtained from a simulation previously conducted with the CLIMBER-2 earth system model of intermediate complexity. Our 3 Ma simulation consists of 42 transient interglacial/glacial simulation chunks, which were partly run in parallel to save computing time. The chunks were subsequently merged, accounting for spin-up and overlap effects to yield a quasi-continuous trajectory. The computer model data were compared against a plethora of paleo-proxy data and large-scale climate reconstructions. For the period from the Mid-Pleistocene Transition (MPT, ∼1 Ma) to the late Pleistocene we find good agreement between simulated and reconstructed temperatures in terms of phase and amplitude (−5.7 ∘C temperature difference between Last Glacial Maximum and Holocene). For the earlier part (3–1 Ma), differences in orbital-scale variability occur between model simulation and the reconstructions, indicating potential biases in the applied CO2 forcing. Our model-proxy data comparison also extends to the westerlies, which show unexpectedly large variance on precessional timescales, and hydroclimate variables in major monsoon regions. Eccentricity-modulated precessional variability is also responsible for the simulated changes in the amplitude and flavors of the El Niño–Southern Oscillation. We further identify two major modes of planetary energy transport, which played a crucial role in Pleistocene climate variability: the first obliquity and CO2-driven mode is linked to changes in the Equator-to-pole temperature gradient; the second mode regulates the interhemispheric heat imbalance in unison with the eccentricity-modulated precession cycle. During the MPT, a pronounced qualitative shift occurs in the second mode of planetary energy transport: the post-MPT eccentricity-paced variability synchronizes with the CO2 forced signal. This synchronized feature is coherent with changes in global atmospheric and ocean circulations, which might contribute to an intensification of glacial cycle feedbacks and amplitudes. Comparison of this paleo-simulation with greenhouse warming simulations reveals that for an RCP8.5 greenhouse gas emission scenario, the projected global mean surface temperature changes over the next 7 decades would be comparable to the late Pleistocene glacial-interglacial range; but the anthropogenic warming rate will exceed any previous ones by a factor of ∼100.

A Multicloud Model for Coastal Convection

Coastal convection is often organized into multiple mesoscale systems that propagate in either direction across the coastline (i.e., landward and oceanward). These systems interact non-trivially with synoptic and intraseasonal disturbances such as convectively coupled waves and the Madden–Julian oscillation. Despite numerous theoretical and observational efforts to understand coastal convection, global climate models still fail to represent it adequately, mainly because of limitations in spatial resolution and shortcomings in the underlying cumulus parameterization schemes. Here, we use a simplified climate model of intermediate complexity to simulate coastal convection under the influence of the diurnal cycle of solar heating. Convection is parameterized via a stochastic multicloud model (SMCM), which mimics the subgrid dynamics of organized convection due to interactions (through the environment) between the cloud types that characterize organized tropical convection. Numerical results demonstrate that the model is able to capture the key modes of coastal convection variability, such as the diurnal cycle of convection and the accompanying sea and land breeze reversals, the slowly propagating mesoscale convective systems that move from land to ocean and vice-versa, and numerous moisture-coupled gravity wave modes. The physical features of the simulated modes, such as their propagation speeds, the timing of rainfall peaks, the penetration of the sea and land breezes, and how they are affected by the latitudinal variation in the Coriolis force, are generally consistent with existing theoretical and observational studies.

Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model–data fusion

Abstract. Many terrestrial landscapes are heterogeneous. Mixed land cover and land use generate a complex mosaic of fragmented ecosystems at fine spatial resolutions with contrasting ecosystem stocks, traits, and processes, each differently sensitive to environmental and human factors. Representing spatial complexity within terrestrial ecosystem models is a key challenge for understanding regional carbon dynamics, their sensitivity to environmental gradients, and their resilience in the face of climate change. Heterogeneity underpins this challenge due to the trade-off between the fidelity of ecosystem representation within modelling frameworks and the computational capacity required for fine-scale model calibration and simulation. We directly address this challenge by quantifying the sensitivity of simulated carbon fluxes in a mixed-use landscape in the UK to the spatial resolution of the model analysis. We test two different approaches for combining Earth observation (EO) data into the CARDAMOM model–data fusion (MDF) framework, assimilating time series of satellite-based EO-derived estimates of ecosystem leaf area and biomass stocks to constrain estimates of model parameters and their uncertainty for an intermediate complexity model of the terrestrial C cycle. In the first approach, ecosystems are calibrated and simulated at pixel level, representing a “community average” of the encompassed land cover and management. This represents our baseline approach. In the second, we stratify each pixel based on land cover (e.g. coniferous forest, arable/pasture) and calibrate the model independently using EO data specific to each stratum. We test the scale dependence of these approaches for grid resolutions spanning 1 to 0.05∘ over a mixed-land-use region of the UK. Our analyses indicate that spatial resolution matters for MDF. Under the community average baseline approach biological C fluxes (gross primary productivity, Reco) simulated by CARDAMOM are relatively insensitive to resolution. However, disturbance fluxes exhibit scale variance that increases with greater landscape fragmentation and for coarser model domains. In contrast, stratification of assimilated data based on fine-resolution land use distributions resolved the resolution dependence, leading to disturbance fluxes that were 40 %–100 % higher than the baseline experiments. The differences in the simulated disturbance fluxes result in estimates of the terrestrial carbon balance in the stratified experiment that suggest a weaker C sink compared to the baseline experiment. We also find that stratifying the model domain based on land use leads to differences in the retrieved parameters that reflect variations in ecosystem function between neighbouring areas of contrasting land use. The emergent differences in model parameters between land use strata give rise to divergent responses to future climate change. Accounting for fine-scale structure in heterogeneous landscapes (e.g. stratification) is therefore vital for ensuring the ecological fidelity of large-scale MDF frameworks. The need for stratification arises because land use places strong controls on the spatial distribution of carbon stocks and plant functional traits and on the ecological processes controlling the fluxes of C through landscapes, particularly those related to management and disturbance. Given the importance of disturbance to global terrestrial C fluxes, together with the widespread increase in fragmentation of forest landscapes, these results carry broader significance for the application of MDF frameworks to constrain the terrestrial C balance at regional and national scales.

Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM

Abstract. Nitrogen (N) and phosphorus (P) biogeochemical dynamics are crucial for the regulation of the terrestrial carbon cycle. In Earth system models (ESMs) the implementation of nutrient limitations has been shown to improve the carbon cycle feedback representation and, hence, the fidelity of the response of land to simulated atmospheric CO2 rise. Here we aimed to implement a terrestrial N and P cycle in an Earth system model of intermediate complexity to improve projections of future CO2 fertilization feedbacks. The N cycle is an improved version of the Wania et al. (2012) N module, with enforcement of N mass conservation and the merger with a deep land-surface and wetland module that allows for the estimation of N2O and NO fluxes. The N cycle module estimates fluxes from three organic (litter, soil organic matter and vegetation) and two inorganic (NH4+ and NO3-) pools and accounts for inputs from biological N fixation and N deposition. The P cycle module contains the same organic pools with one inorganic P pool; it estimates influx of P from rock weathering and losses from leaching and occlusion. Two historical simulations are carried out for the different nutrient limitation setups of the model: carbon and nitrogen (CN), as well as carbon, nitrogen and phosphorus (CNP), with a baseline carbon-only simulation. The improved N cycle module now conserves mass, and the added fluxes (NO and N2O), along with the N and P pools, are within the range of other studies and literature. For the years 2001–2015 the nutrient limitation resulted in a reduction of gross primary productivity (GPP) from the carbon-only value of 143 to 130 Pg C yr−1 in the CN version and 127 Pg C yr−1 in the CNP version. This implies that the model efficiently represents a nutrient limitation over the CO2 fertilization effect. CNP simulation resulted in a reduction of 11 % of the mean GPP and a reduction of 23 % of the vegetation biomass compared to the baseline C simulation. These results are in better agreement with observations, particularly in tropical regions where P limitation is known to be important. In summary, the implementation of the N and P cycle has successfully enforced a nutrient limitation in the terrestrial system, which has now reduced the primary productivity and the capacity of land to take up atmospheric carbon, better matching observations.