Gravity-driven flow of large ice masses such as the Antarctic Ice Sheet (AIS) depends on both the geometry and the mass density of the ice sheet. The vertical density profile can be approximated as pure ice overlain by a firn layer of varying thickness, and for the AIS the firn thickness is not uncommonly 10 to 20% of the total thickness, leading to not insignificant variation in density. Nevertheless, in most vertically-integrated ice-flow models today the density is assumed to be constant, sometimes with an adjustment in thickness to compensate. In this study, we explore the treatment of horizontal density variations (HDVs) within vertically-integrated ice-sheet models. We assess the relative merits and shortcomings of previously proposed approaches, and provide new formulations for including HDVs. We use perturbation analysis to derive analytical solutions that describe the impact of density variations on ice flow for both grounded ice and floating ice shelves. Our analytical solutions reveal significant qualitative differences between each of the proposed density formulations. Furthermore, by modelling the transient evolution of a large sector of the West Antarctic Ice Sheet (WAIS), we quantify the impact of HDVs on estimated sea level change. For WAIS we find that explicitly including the horizontal density gradients in the momentum and mass conservation equations leads to about a 10% correction in the estimated change in volume above flotation over 40 years. We conclude that including horizontal density variations in flow modelling of the Antarctic Ice Sheet is important for accurate predictions of mass loss.

Part of the economic recovery plans implemented by governments following COVID-19 is directed towards the energy transition. To understand the potential effects of these post-COVID green recovery packages on reductions of greenhouse gases emissions, we investigated three different approaches. Firstly, we analysed simulation results of Integrated Assessment Models (IAMs) to infer the change in CO2 intensity of GDP that could result from post-COVID low-carbon investment plans. Secondly, we investigated the scenarios provided by the International Energy Agency (IEA) based on a bottom-up energy system model. By combining the two approaches, we found that green recovery packages implemented and planned globally can lead to an emission reduction of merely 1%-6% from 2030 baseline levels at most. Thirdly, we looked into the results of the Adaptative Regional Input-Output model, which simulates the dynamic effects of economic crisis and fiscal stimuli through supply chains following labour shortage. The third approach shows that the increase of activity driven by fiscal stimuli leads to a rebound of CO2 emissions even if they do not target carbon-intensive sectors. We conclude that green recovery packages targeting low-carbon technologies have a limited impact on near-term CO2 emissions and that demand-side incentives, as well as other policy efforts to disincentivise the use of fossil fuels, are also important for scaling up climate mitigation.

The Blob was a marine heat wave in the Northeast Pacific from 2013 to 2016. While the upper ocean temperature in the Blob has been well described, the impacts on marine biogeochemistry have not been fully studied. Here, we characterize and develop understanding of Eastern North Pacific upper ocean biogeochemical properties during the Winter of 2013-14 using in situ observations, an observation-based product, and reconstructions from a collection of ocean models. We find that the Blob is associated with significant upper ocean biogeochemical anomalies: a 5% increase in aragonite saturation state (temporary reprieve of ocean acidification) and a 3% decrease in oxygen concentration (enhanced deoxygenation). Anomalous advection and mixing drives the aragonite saturation anomaly, while anomalous heating and air-sea gas exchange drive the oxygen anomaly. Marine heatwaves do not necessarily serve as an analogue for future change as they may enhance or mitigate long-term trends.

The 2018 outbreak of dengue in the French overseas department of Réunion was unprecedented in size and mobility across the island. This research focuses on the cause of the outbreak, asserting that climate played a large role in both the proliferation of the mosquitoes, which transmitted the disease, and the vulnerability of the island’s occupants. Additionally, this study analyses if this outbreak could have been forecast in the sub-seasonal time scale. A stage-structured model was run using observed temperature and rainfall data to simulate the lifecycle and abundance of the mosquito. Further, the model was forced with uncalibrated sub-seasonal forecasts to determine if the event could have been forecast up to four weeks in advance. With unseasonably warm temperatures remaining above 25 degrees C, along with large tropical-cyclone-related rainfall events accumulating 10-15 mm per event, the modeled mosquito abundance did not decrease during the second half of 2017, contrary to the normal behavior, likely contributing to the large dengue outbreak in early 2018. Although sub-seasonal forecasts of rainfall for the Dec-Jan period in Réunion are skillful up to four weeks in advance, the outbreak could only have been forecast two weeks in advance, which along with seasonal forecast information could have provided enough time to enhance preparedness measures. Our research demonstrates the potential of using state-of-the-art sub-seasonal climate forecasts to produce actionable sub-seasonal dengue predictions. To the best of the authors’ knowledge, this is the first time sub-seasonal forecasts have been used this way.

Reducing nitrous oxide (N2O) emissions from agriculture is critical to limiting future global warming. In response, a growing number of food retailers and manufacturers have committed to reducing N2O emissions from their vast networks of farmer suppliers by providing technical assistance and financial incentives. A key challenge for such companies is demonstrating that their efforts are leading to meaningful progress towards their climate mitigation commitments. We show that a simplified version of soil surface nitrogen (N) balance, the difference between N inputs to and outputs from a farm field (e.g., fertilizer N minus crop N), is a robust indicator of N2O emissions. Furthermore, we present a generalized environmental model which will allow food-supply-chain companies to translate aggregated and anonymized changes in average N balance across their supplying farms into aggregated changes in N2O emissions. This research is an important first step, based on currently available science, in helping companies demonstrate the impact of their sustainability efforts.

The ratification of Sustainable Development Goals (SDGs) by all member countries of the United Nations demonstrates the determination of the international community in moving towards a sustainable future. To enable and encourage accountability, independent and transparent measurements of national sustainability efforts are essential. Among all sectors, agriculture is fundamental to all three pillars of sustainability, namely environment, society, and economy. However, the definition of a sustainable agriculture and the feasibility of measuring it remain elusive, in part because it encompasses both biophysical and socio-economic components that are still poorly integrated. Therefore, we have been developing a Sustainable Agriculture Matrix (SAM) on a national scale in order to measure country-level performance in agriculture. First proposed by Swaminathan for agricultural research and policy in 1990s, SAM is a collection of indicators measuring sustainable agriculture from environmental, social, and economic dimensions (Table 1). Specifically, from an Environment perspective, sustainable agriculture reduces unsustainable use of water resources for agricultural production, further loss of biodiversity from converting native habits to agriculture, production of forms of pollution that affect local and regional water and air quality, and emissions of greenhouse gases, and it maintains or improves soil health and fertility. From an Economic perspective, sustainable agriculture improves the economic viability of the agricultural sector by enhancing agricultural productivity and profitability, advancing agricultural innovation capacity, providing farmers access to market and credits, reducing farmers’ risks. From a Social perspective, sustainable agriculture improves farmers’ wellbeing, respects farmers’ rights, promotes equitable opportunities, and benefits the whole society with enhanced system resilience and improved health and nutrition. Translating the illustrative concepts into measurable indicators will not only provide an independent and transparent measurement of national performance in the sustainability of agriculture production, which is at the center of Water-Energy-Food nexus, but also provide timely information to help guide evolving national policies regarding agriculture, trade, environment, and national security.

While many studies comparing atmospheric reanalysis and surface observations have focused on the similarity of mean fields, trends, or frequencies of extreme events, very few have assessed how similar surface observations and reanalysis data sets are in terms of their specific identification of extreme temperature event days. Here, we assess the similarity between surface observations and three reanalysis products: ERA5, ERA5-LAND, and NARR, in terms of the days on which they identify extreme temperature events. We assess similarity from 1979-2016 for 231 locations in the United States and Canada, assessing Extreme Heat and Cold Event days, as well as their counterpart events that are relative for the time of year. Cold Events have a greater match than Heat Events. ERA5 has the greatest match percentage with station data across the study region. Match percentage is greatest in mid-latitude, continental locations, with poorer performance in coastal areas, and the Arctic.

This research is part of the ongoing research project − Climate change Adaptation to ManagE the risks of extreme hydrologicaL and weather events for food security in vulnerable west Nile delta (CAMEL). The study area, West Nile Delta, is an important region in Egypt featuring agricultural and industrial significance to the nation, whilst it faces serious crises from the interaction of complex environmental problems (e.g. flooding) which is exacerbated by climate change in the recent decades. Under the pressure of growing population, food security has become a national issue. In the latest decades, the region has experienced more extreme weather events; the severe rainfall events have resulted in flooding destroying massive crops and causing losses of human life and livestock. The evolvement of society in this region has made the people living in the flood-prone area – mostly farm labours − relatively socio-economic vulnerable. This research hence focuses on the urgent foregoing issue − disastrous pluvial flooding, which seeks to mitigate the issue of crop production loss and human casualty caused by climate change. Therefore, an adaption measure of an early warning system for extreme events caused by heavy rainfall has become an urgent demand. However, the scarcity of data (e.g. insufficiency in the coverage of gauge stations and radar stations) has always been a main obstacle to relevant measures in Egypt. The research hence seeks to cope with such difficulty whilst to build an integrated flood early warning system for Egypt. Based on the integration of Nowcasting method (applying GPM and MPE satellite radar observation) and NWP method (downscaling ECMWF data) as the substitution for the insufficient ground observations, the integrated approach can take the advantages of both data sources to perform better forecasting. However, GPM and MPE data, compared with ground observation data, still reflects relative disadvantages in spatial and temporal resolution in terms of Nowcasting application. Besides, notwithstanding Nowcasting method can make up for the spatial resolution of the NWP method, its mainstream − optical flow approach based on the Lagrangian method − still lacks confidence in dealing with local advection circumstance, as well as fast and drastic formation and dissipation of precipitation. The research hence seeks to improve Nowcasting, by applying a phase-based frame interpolation method based on the Eulerian method, to refine the resolution of data to improve the performance of Nowcasting. It features better performance in precipitation change, strong precipitation divergence (i.e. light contrast), and computational efficiency. The improved Nowcasting, for further integrating with the NWP method, is being tested and proposed, which will end up with a recommendation of policy and a novel tool – real-time flood early warning system – so as to accommodate the hydrological extremes towards climate change in Egypt.

Long, continuous palaeoclimate records provide an opportunity to extend knowledge of decadal to multi-decadal scale climate variability beyond the limit of instrumental records. In this study, quality-controlled proxy records from southeastern Australia are examined for coherent variability during the Common Era, with age uncertainty for each record estimated using iterative age modeling. Site-level empirical orthogonal functions (EOFs) are derived from multivariate records for the purpose of objective comparison of climate signals between sites without selection bias. A regional Monte Carlo EOF (MCEOF) analysis is conducted on combined time-uncertain single-proxy records and site-level EOFs. The analysis identifies two robust vectors, which are inferred to represent hydroclimate changes. The first regional MCEOF suggests an increase in effective moisture between 900 – 1750 CE. Agreement between regional MCEOF1 and Australian temperature reconstructions suggests suppressed evaporation was a significant influence on regional effective moisture during this time. Regional MCEOF2 exhibits shorter, centennial-scale oscillations that show some similarity with rainfall reconstructions based on remote high-resolution proxies. We interpret MCEOF2 to represent regional-scale rainfall patterns driven by changes in seasonal rainfall and the influence of the Southern Annular Mode over southern Australian rainfall. This study presents the first quantitative regional synthesis of southeastern Australian hydroclimate reconstructions from multivariate sedimentary archives covering the last 1200 years. The resulting MCEOFs demonstrate the utility of low-resolution climate records from this region, but also highlight the limitations of the existing data network, which must be resolved through the generation of new records.

Many indigenous peoples are working to maintain cultural survival through integration of indigenous knowledge (e.g., phenological observations, wild cultivation expertise, and ecosystem management expertise) with climate change research and climate based ecological restoration/adaptation. Even though local, place-based climate knowledge maintained through story and knowledge of sacred sites, ancestral gathering, hunting, camping and fishing areas are incredibly valuable for climate change adaptation planning, this information is not readily transferable to the scientific literature, and in most cases, it would be inappropriate or offensive to publish. This presents a challenge for those working to blend traditional knowledge and western science during the development of climate change adaptation programs and collaborative relationships with scientific and educational institutions. The Nez Perce Tribe is working to overcome these barriers through meaningful community participation, surveys and elder interviews, hiring in-house social and natural science professionals, climate smart conservation projects that include cultural values without revealing sacred information, and the leadership and grace of the Tribal Community, Government, and Staff. We present a case study on collaborations with Point Blue Conservation Science and the University of Idaho to include cultural traditions and values in a restoration toolkit for ecological and cultural resilience, and a climate-smart agricultural program. We discuss the steps taken by the Tribe to overcome barriers, lessons learned, suggestions for methodologies, and measures to honor the resilience, wisdom, and wishes of the Nez Perce People during this process. Our collective future depends upon collaborations amongst human beings with a checkered collective past, and bold and courageous leadership. Tribal communities are demonstrating a model of leadership and grace by working with each other and with western scientists in visionary ways on climate change resilience planning. This has allowed for collaborative relationships that are expanding the capacity of the Nez Perce Tribe to address climate change and integrate cultural values and perspectives into the process.

The livestock sector is the largest source of anthropogenic methane emissions, and is projected to increase in the future with increased demand for livestock products. Here, we compare livestock methane emissions and emission intensities, defined by the amount of methane emitted per unit of animal proteins, estimated by different methodologies, and identify mitigation potentials in different regions of the world based on possible future projections. We show that emission intensity decreased for most livestock categories globally during 2000-2018, due to an increasing protein-production efficiency, and the IPCC Tier 2 method should be used for capturing the temporal changes in the emission intensities. We further show that efforts on the demand-side to promote balanced, healthy and environmentally-sustainable diets in most countries will not be sufficient to mitigate livestock methane emissions without parallel efforts to improve production efficiency. The latter efforts have much greater mitigating effects than demand-side efforts, and hence should be prioritized in a few developing countries that contribute most of the mitigation potential.

Relative sea-level rise is a major coastal hazard affecting about half the population of the United States. The Chesapeake Bay is characterized by the fastest rate of sea-level rise along the Atlantic coast of North America, in part because of land subsidence. Previous studies have quantified a range of land subsidence rates in the Chesapeake Bay (~1-4 mm/yr) from various measurement techniques that contribute to high rates of relative sea-level rise. In this study, we present progress towards developing a new vertical land motion map for the Chesapeake Bay region to provide more robust constraints on estimates of relative sea-level rise. We are using a combination of GNSS observations and InSAR interferograms. Available continuous GNSS data in the region that span November 2014 - September 2020 are processed with GAMIT-GLOBK to align temporally with available Sentinel-1 InSAR satellite data. We are using an approach that combines the two geodetic observations to provide a new solution of vertical land motions for the Chesapeake Bay. Additionally, this project is collecting new campaign GNSS observations across the Chesapeake Bay each fall for 5 years, beginning in 2019. We will also present about the 2020 and planned 2021 campaign GNSS observations, which will ultimately be incorporated into our new map of vertical land motions for the region. The impacts of this work will be improved flooding and inundation hazard maps, as well as updated projections for municipal flood mitigation planning that will be created using the new dataset.

Preservation of organic carbon (OC) in marine sediments exerts a major control on the cycling of carbon in the Earth system. In marine sediment, OC preservation may be enhanced by diagenetic reactions in locations where deposition of tephra occurs. While the mechanisms by which this process occurs are well understood, site-specific studies are limited. Here, we report on a study of sediments from the Bering Sea (IODP Site U1339D) to investigate the effects of marine tephra deposition on carbon cycling during the Pleistocene and Holocene. Our results strongly suggest that tephra layers are loci of OC burial with distinct d13C values, and that this process is primarily linked to complexation of OC with reactive metals (accounting for ~80% of all OC within tephra layers). In addition, distribution of reactive metals into non-volcanic sediments above and below the tephra layers enhances OC preservation in these sediments, with ~33% of OC bound to reactive phases. Importantly, OC-Fe coupling is evident in sediments >700,000 years old. Thus, these interactions may help explain the preservation of labile OC in older marine sediments.

We quantify the volume transport and watermass transformation rates of the global ocean circulation using data from the Estimating the Circulation and Climate of the Ocean version 4 release 3 (ECCOv4r3) reanalysis product. Our results support large rates of intercell exchange between the mid-depth and abyssal cells, in agreement with modern theory and observations. However, the present-day circulation in ECCO cannot be interpreted as a near-equilibrium solution. Instead, a dominant portion of the apparent diapycnal transport of watermasses within the deep ocean is associated with isopycnal volume change, rather than diabatic processes, reflecting trends in the deep ocean density structure. Our results imply two possibilities: either such trends in ECCOv4r3 are unrealistic, implying that ECCO’s representation of the overturning circulation and watermass transformations are inconsistent, or the trends in ECCOv4r3 are realistic and equilibrium theories of the overturning circulation cannot be applied to the present-day ocean.

The radiative cooling rate in the tropical upper troposphere is expected to increase as climate warms. Since the tropics are approximately in radiative-convective equilibrium (RCE), this implies an increase in the convective heating rate, which is the sum of the latent heating rate and the eddy heat flux convergence. We examine the impact of these changes on the vertical profile of cloud ice amount in cloud-resolving simulations of RCE. Three simulations are conducted: a control run, a warming run, and an experimental run in which there is no warming but a temperature forcing is imposed to mimic the warming-induced increase in radiative cooling. Surface warming causes a reduction in cloud fraction at all upper tropospheric temperature levels but an increase in the ice mixing ratio within deep convective cores. The experimental run has more cloud ice than the warming run at fixed temperature despite the fact that their latent heating rates are equal, which suggests that the efficiency of latent heating by cloud ice increases with warming. An analytic expression relating the ice-related latent heating rate to a number of other factors is derived and used to understand the model results. This reveals that the increase in latent heating efficiency is driven mostly by 1) the migration of isotherms to lower pressure and 2) a slight warming of the top of the convective layer. These physically robust changes act to reduce the residence time of ice along at any particular temperature level, which tempers the response of the mean cloud ice profile to warming.

Partitioning evapotranspiration (ET) into its primary components, i.e., evaporation (E) and plant transpiration (T), is needed in a range of hydrometeorological applications. Using vegetation index (VI) to obtain spatially resolved T:ET ratio over large areas has emerged as a promising approach in this regard. Here, we assess the effectiveness of this approach in differently managed wheat systems. Results show a weak relation between T:ET and VI in disturbed (i.e., grazed) systems. Flux partitions based on a canonical T:ET vs. VI relation or one derived in a neighboring undisturbed wheat system introduce large errors in disturbed systems, thus underscoring the limits on the transferability of the VI-based ET partitioning approach. The effectiveness of the VI-based approach is found to be related to the strength of correlation between VI and vapor pressure deficit and/or radiation. This correlation metric can help identify settings where the approach is likely to be effective.

Volcanic aerosol forcing has been reported in the literature to be less effective in changing the earth’s surface temperature than CO2 forcing. This implies a different feedback strength, and therefore different contributions from individual feedback mechanisms. We employ the CMIP6 version of MPI-ESM to understand the reasons for these apparent differences in the ability to change the surface air temperature. Using a highly idealized eruption scenario and comparing it to a doubling and a halving of CO2 concentration, we identify key reasons for changes in the magnitude of the feedback parameter. We show that the “efficacy” [Hansen et al. 2005] of volcanic aerosol forcing depends strongly on the method and the time scale used to calculate it. We argue that the seemingly established result of a lower-than-unity efficacy of volcanic aerosol forcing might only hold under the specific methodological choices other authors have made, but not in general. Furthermore, we find qualitative differences between the cooling and warming simulations, but strong similarities between the 0.5xCO2 and the idealized eruption cases. This hints towards processes, which are not forcing agent-specific, but specific to the sign of the forcing. A pronounced curvature in the N(T) plot (“Gregory plot”) for the cooling scenarios makes the computation of feedback through regression even more sensitive to subjective choices than in the 2xCO2 case. We disentangle the role of ocean heat uptake efficacy and atmospheric feedback processes in the framework of the pattern effect.

Ocean waves at any particular location are the result of the superposition of locally generated waves by wind, plus swells advected from somewhere else. Swells in particular can travel very long distances with marginal energy loss such that their signal, albeit reduced by dispersion, can be detected all across the oceans. Although our current approach for wave modeling and description has the wave spectrum as standard variable, most wave characterization methods are based on simplified integral parameters (e.g., Hs, Tm). These are indicative of the overall magnitude, but loose all the information stored in the spectral structure. Therefore, total wave fields derived from integral parameters are smooth and continuous while in reality wave fields have well defined spatial domains, they overlap one another, and they vary significantly along the seasons in response to the ever changing meteorological forcing. Using spectral partitioning techniques and the global spectral wave climate atlas GLOSWAC, the main wave fields active in the different ocean basins can be elucidated and separated from the integrated one. As the memory of the sea surface (waves) is longer than that of the atmosphere, these individual wave fields constitute a valuable new source of environmental information, and its characterization opens the way to more advanced wave analysis methods.

Carbonate clumped isotope thermometry has been calibrated for a wide variety of carbonates, including calcite, aragonite, dolomite, siderite, and many of their biogenic forms. The clumped isotope composition of the carbonate group substituting for phosphate or hydroxyl in bioapatite (Ca(PO4,CO3)(OH,F)) has also been temperature calibrated using vertebrate tooth enamel from a range of endothermic body temperatures. We apply this method to other bioapatite-bearing taxa and the calibrated temperature range is extended to lower paleoclimatologically relevant temperatures. Furthermore, because relatively large bioapatite samples are required for carbonate clumped isotope measurements (Δ47), replicate sampling of thin tooth enamel may not be feasible in many situations. Here, we use gar fish (Lepisosteus sp.) scales to extend the calibration. These fish are unique in that they are entirely covered in ganoine scales, which are >95% hydroxyapatite. Their enamel structure also makes them resistant to diagenesis. Additionally, gar fossils are common in lacustrine, fluvial, and near-shore facies, and have a wide distribution in time (Cretaceous to modern) and location (North America, South America, Europe, India, and Africa). We have developed a reliable lab protocol for measuring Δ47 in gar bioapatite. We estimate the standard error (SE) for a single measurement as 0.027‰, which is based on replicate analyses and Student T-distribution to account for sample size. We report results for modern gar scales from seven North American localities with mean annual water temperatures (MAWT) ranging from 9 to 26 °C. These data give a temperature calibration curve for gar scales of Δ47 = (0.1095 ± 0.0159) x 106/T2 – (0.5941 ± 0.0548) (R2 = 0.74) and a curve for pooled bioapatite of Δ47 = (0.1003 ± 0.0144) x 106/T2 – (0.4873 ± 0.0495) (R2 = 0.76).

Recent studies have documented that the Madden-Julian Oscillation (MJO) has impacts in extreme dry/wet conditions over tropical regions and in atmospheric state. They are based, however, in correlation analysis, and therefore do not consider non-linear interactions nor they establish cause-effect relationships.In this study we introduce a generalization of the non-linear Granger causality (GC) test to identify causal relations between MJO and hydrological extremes. The method is able to identify causal relations under noisy, nonlinear and non-stationary scenarios. A probabilistic extension is also introduced where the causal test operates directly on the marginal likelihood (also called evidence) of the observations, which is analytic. We apply our proposed method to MJO and satellite-based soil moisture (SM) data, and revisit the global teleconnection patterns induced by MJO events. Since El Ni\~no Southern Oscillation (ENSO) is a modulating factor that can result in abnormal SM global distributions, we also include it in the analysis as a potential driver of SM variability. Including ENSO allows us to differentiate the effect of the MJO and ENSO on the global SM anomalies and to learn the causal graph of their cause-effect relationships, and also the mutual relation between MJO and ENSO extreme events.

A new methodology for satellite retrieval of cloud condensation nuclei (CCN) in shallow marine boundary layer clouds is developed and validated in this study. The methodology is based on retrieving cloud base drop concentrations (Nd) and updrafts (Wb), and calculating the supersaturation (S) based on that. Then Nd is the CCN at S. 50 The accuracy of the satellite retrievals was validated against ship borne measurements of CCN done in recent campaigns in the Southern Oceans (ACE-SPACE, MARCUS & PEGASO [2015-2018]) and in the subtropics (MAGIC [2012-2013]). The satellite retrieve Nd and S at cloud base was related to the actually measured CCN(S) at sea surface. The main findings show that: (a) coupled clouds have good agreement 55 between satellite retrievals and ship measurements of CCN(S); (b) the best agreement is achieved when using the brightest 10% of the clouds, and accounting for their adiabatic fraction, as measured by aircrafts; (c) most of the decoupled clouds had much lower CCN(S) than at the underlying surface. This means that most CCN originate from the surface and not from the free troposphere. This validates the satellite retrievals and 60 allows us to further quantify the relationships between CCN(S) and cloud microphysical properties.

The California Council on Science and Technology (CCST) is a non-partisan, nonprofit, boundary organization with the mission of bringing science to decisionmakers. We focus on building and strengthening relationships with California’s decisionmakers—who need information to craft fact-based policy—and the scientists and experts who generate knowledge. We connect science and policy through a variety of programs, including 1) rapid-response expert briefings for the California capitol community on emerging issues, such as wildfires or disease outbreaks; 2) peer-reviewed, independent studies commissioned by State entities to provide decisionmakers with comprehensive analyses of the state of science on politically relevant, technically complex topics; and 3) a Science Fellowship program that for the past 10 years has placed PhD scientists and engineers as staff in State legislative offices for a year of public service. We will share specific examples from our programs that highlight best practices for facilitating the transfer of knowledge between scientists and decisionmakers and lessons learned from navigating the barriers that commonly arise when working at the boundary of science and policy.

The diamondback terrapin (Malaclemys terrapin), the official state reptile of Maryland, is an estuarine turtle found along the eastern and Gulf coasts of the United States. The diamondback terrapin may experience risks due to increasing global temperatures because they display temperature-dependent sex determination (TSD). Terrapins, through TSD, produce more female offspring than male offspring if their nest temperatures are relatively warm, leading to ecological issues should global temperatures get too high. This study used temperatures from several individual field, monitored nests, along with air temperatures at the nesting site, recorded in the summer of 2018, to select a predictive model with the R software package rstanarm. The model was then used to predict the average nest temperature in the same nesting site using air temperatures that were recorded during the summer of 2019. This prediction was then compared to temperatures recorded from five nests within the nesting site in 2019 to evaluate its predictive capabilities. A regression spline mixed model (RSMM) was selected for having the lowest leave-one-out information criterion at 50905.95 LOOIC, with a standard error of 0.64 LOOIC. The prediction of average nest temperature developed by the RSMM was given a predictive interval of 95%. We found that the actual temperature of the average nest in 2019 had a prediction coverage of 92.97% from the prediction model, with a root squared mean error of 1.89oC and a predictive mean absolute error of 2.10oC. Benefits of the study involve the ability to predict the future diamondback terrapin nest temperatures within a specific nesting site so long as future air temperatures within that site are known.

In the TRACMIP ensemble of aquaplanet climate model experiments, CO2-induced warming is amplified in the poles in 10 out of 12 models, despite the lack of sea ice. We attribute causes of this amplification by perturbing individual radiative forcing and feedback components in a moist energy balance model. We find a strikingly linear pattern of tropical versus polar warming contributions across models and processes, implying that polar amplification is an inherent consequence of diffusion of moist static energy by the atmosphere. The largest contributor to polar amplification is the instantaneous CO2 forcing, followed by the water vapor feedback and, for some models, cloud feedbacks. Extratropical feedbacks affect polar amplification more strongly, but even feedbacks confined to the tropics can cause polar amplification. Our results contradict studies inferring warming contributions directly from the meridional gradient of radiative perturbations, highlighting the importance of interactions between feedbacks and moisture transport for polar amplification.