Land surface conductance linked to precipitation: Co-evolution of vegetation and climate in Earth system models.

Vegetation and precipitation are known to fundamentally influence each other. However, this interdependence is not fully represented in climate models because the characteristics of land surface (canopy) conductance to water vapor and CO2 are determined independently of precipitation. Working within a coupled atmosphere and land modelling framework (CAM6/CLM5; coupled Community Atmosphere Model v6/Community Land Model v5), we have developed a new theoretical approach to characterizing land surface conductance by explicitly linking its dynamic properties to local precipitation, a robust proxy for moisture available to vegetation. This will enable regional surface conductance characteristics to shift fluidly with climate change in simulations, consistent with general principles of co-evolution of vegetation and climate. Testing within the CAM6/CLM5 framework shows that climate simulations incorporating the new theory outperform current default configurations across several error metrics for core output variables when measured against observational data. In climate simulations for the end of this century the new, adaptive stomatal conductance scheme provides a revised prognosis for average and extreme temperatures over several large regions, with increased primary productivity through central and east Asia, and higher rainfall through North Africa and the Middle East. The new projections also reveal more frequent heatwaves than originally estimated for the south-eastern US and sub-Saharan Africa but less frequent heatwaves across east Europe and northeast Asia. These developments have implications for evaluating food security and risks from extreme temperatures in areas that are vulnerable to climate change.

Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions.

Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon-nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and-the newly developed-5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO2) enrichment with meta-analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET-MTE observations. Simulations with N and CO2 enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections.

Comparing optimal and empirical stomatal conductance models for application in Earth system models.

Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, and CO2 fluxes. This is achieved by scaling stomatal conductance, gw , determined from physiological models developed for leaves. Traditionally, models for gw have been semi-empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to model gw in LSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization-type gw model, termed BB and MED, respectively. Overall, we find no difference between the two models when used to simulate gw from photosynthesis data, or leaf gas exchange from a coupled photosynthesis-conductance model, or gross primary productivity and evapotranspiration for a FLUXNET tower site with the CLM5 community LSM. Field measurements reveal that the key fitted parameters for BB and MED, g1B and g1M, exhibit strong species specificity in magnitude and sensitivity to CO2 , and CLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high-CO2 scenarios. Further, we show that g1B and g1M can be determined from mean ci /ca (ratio of leaf intercellular to ambient CO2 concentration). Applying this relationship with ci /ca values derived from a leaf δ13 C database, we obtain a global distribution of g1B and g1M , and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of the BB and MED models in LSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types.

Large model structural uncertainty in global projections of urban heat waves.

Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Here, we use an urban climate emulator combined with large ensemble global climate simulations to show that, at the urban scale a large proportion of the variability results from the model structural uncertainty in projecting UHWs in the coming decades under climate change. Omission of this uncertainty would considerably underestimate the risk of UHW. Results show that, for cities in four high-stake regions - the Great Lakes of North America, Southern Europe, Central India, and North China - a virtually unlikely (0.01% probability) UHW projected by single-model ensembles is estimated by our model with probabilities of 23.73%, 4.24%, 1.56%, and 14.76% respectively in 2061-2070 under a high-emission scenario. Our findings suggest that for urban-scale extremes, policymakers and stakeholders will have to plan for larger uncertainties than what a single model predicts if decisions are informed based on urban climate simulations.

Classifying heatwaves: Developing health-based models to predict high-mortality versus moderate United States heatwaves.

Heatwaves are divided between moderate, more common heatwaves and rare "high-mortality" heatwaves that have extremely large health effects per day, which we define as heatwaves with a 20% or higher increase in mortality risk. Better projections of the expected frequency of and exposure to these separate types of heatwaves could help communities optimize heat mitigation and response plans and gauge the potential benefits of limiting climate change. Whether a heatwave is high-mortality or moderate could depend on multiple heatwave characteristics, including intensity, length, and timing. We created heatwave classification models using a heatwave training dataset created using recent (1987-2005) health and weather data from 82 large US urban communities. We built twenty potential classification models and used Monte Carlo cross-validations to evaluate these models. We ultimately identified several models that can adequately classify high-mortality heatwaves. These models can be used to project future trends in high-mortality heatwaves under different scenarios of a changing future (e.g., climate change, population change). Further, these models are novel in the way they allow exploration of different scenarios of adaptation to heat, as they include, as predictive variables, heatwave characteristics that are measured relative to a community's temperature distribution, allowing different adaptation scenarios to be explored by selecting alternative community temperature distributions. The three selected models have been placed on GitHub for use by other researchers, and we use them in a companion paper to project trends in high-mortality heatwaves under different climate, population, and adaptation scenarios.

Projected trends in high-mortality heatwaves under different scenarios of climate, population, and adaptation in 82 US communities.

Some rare heatwaves have extreme daily mortality impacts; moderate heatwaves have lower daily impacts but occur much more frequently at present and so account for large aggregated impacts. We applied health-based models to project trends in high-mortality heatwaves, including proportion of all heatwaves expected to be high-mortality, using the definition that a high-mortality heatwave increases mortality risk by ≥20 %. We projected these trends in 82 US communities in 2061-2080 under two scenarios of climate change (RCP4.5, RCP8.5), two scenarios of population change (SSP3, SSP5), and three scenarios of community adaptation to heat (none, lagged, on-pace) for large- and medium-ensemble versions of the National Center for Atmospheric Research's Community Earth System Model. More high-mortality heatwaves were expected compared to present under all scenarios except on-pace adaptation, and population exposure was expected to increase under all scenarios. At least seven more high-mortality heatwaves were expected in a twenty-year period in the 82 study communities under RCP8.5 than RCP4.5 when assuming no adaptation. However, high-mortality heatwaves were expected to remain <1 % of all heatwaves and heatwave exposure under all scenarios. Projections were most strongly influenced by the adaptation scenario- going from a scenario of on-pace to lagged adaptation or from lagged to no adaptation more than doubled the projected number of and exposure to high-mortality heatwaves. Based on our results, fewer high-mortality heatwaves are expected when following RCP4.5 versus RCP8.5 and under higher levels of adaptation, but high-mortality heatwaves are expected to remain a very small proportion of total heatwave exposure.

Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate.

Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.

The importance of land-cover change in simulating future climates.

Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.