Abstract: Eddy covariance (EC) estimates of carbon dioxide (CO2) fluxes and energy balance are examined to investigate the functional responses of a mature mangrove forest to a disturbance generated by Hurricane Wilma on October 24, 2005 in the Florida Everglades. At the EC site, high winds from the hurricane caused nearly 100% defoliation in the upper canopy and widespread tree mortality. Soil temperatures down to −50 cm increased, and air temperature lapse rates within the forest canopy switched from statically stable to statically unstable conditions following the disturbance. Unstable conditions allowed more efficient transport of water vapor and CO2 from the surface up to the upper canopy layer. Signifi- cant increases in latent heat fluxes (LE) and nighttime net ecosystem exchange (NEE) were also observed and sensible heat fluxes (H) as a proportion of net radiation decreased significantly in response to the disturbance. Many of these impacts persisted through much of the study period through 2009. However, local albedo and MODIS (Moderate Resolution Imaging Spectro-radiometer) data (the Enhanced Vegetation Index) indicated a substantial proportion of active leaf area recovered before the EC measurements began 1 year after the storm. Observed changes in the vertical distribution and the degree of clumping in newly emerged leaves may have affected the energy balance. Direct comparisons of daytime NEE values from before the storm and after our measurements resumed did not show substantial or consistent differences that could be attributed to the disturbance. Regression analyses on seasonal time scales were required to differentiate the storm’s impact onmonthly average daytime NEE fromthe changes caused by interannual variability in other environmental drivers. The effects of the storm were apparent on annual time scales, and CO2 uptake remained approximately 250 g Cm−2 yr−1 lower in 2009 compared to the average annual values measured in 2004–2005. Dry season CO2 uptake was relatively more affected by the disturbance than wet season values. Complex leaf regeneration dynamics on damaged trees during ecosystem recovery are hypothesized to lead to the variable dry versus wet season impacts on daytime NEE. In contrast, nighttime CO2 release (i.e., nighttime respiration) was consistently and significantly greater, possibly as a result of the enhanced decomposition of litter and coarse woody debris generated by the storm, and this effect was most apparent in the wet seasons compared to the dry seasons. The largest pre- and post-storm differences in NEE coincided roughly with the delayed peak in cumulative mortality of stems in 2007–2008. Across the hurricane-impacted region, cumulative tree mortality rates were also closely correlated with declines in peat surface elevation. Mangrove forest–atmosphere interactions are interpreted with respect to the damage and recovery of stand dynamics and soil accretion processes following the hurricane.

Abstract: Recent analyses suggest that considerable CaCO3 dissolution may occur in the upper water column of the ocean (<1500m). This study uses the distribution of particulate calcium from high-resolution suspended matter sampling along the Climate Variability and Predictability/CO2 Repeat Hydrography A16N transect in 2003 to estimate CaCO3 dissolution in the top 1000m of the North Atlantic. Dissolution rates were also approximated using changes in total alkalinity measurements along isopycnal surfaces. Water masses were found to be undersaturated with respect to aragonite at intermediate depths (400-1000m) in the eastern tropical North Atlantic. The CaCO3 dissolution rate in this region is estimated to be 0.9mmol CaCO3 m(-2) d(-1), indicating this region is a hotspot for upper water column CaCO3 dissolution compared to the Atlantic basin as a whole. Dissolution rates calculated from particulate calcium distributions outside of this region were significantly lower (0.2mmol CaCO3 m(-2) d(-1)) and are comparable to previous estimates of CaCO3 dissolution flux for the Atlantic Ocean. The magnitude of upper water column dissolution rates compared to measured surface ocean CaCO3 standing stocks suggests that biologically mediated CaCO3 dissolution may be occurring in the top 1000m of the Atlantic.

Abstract: Within the context of a changing climate, scientists are called to engage directly with agricultural stakeholders for the coproduction of relevant information that will support decision making and adaptation. However, values, beliefs, identities, goals, and social networks shape perceptions and actions about climate change. Engagement processes that ignore the socio-cultural context within which stakeholders are embedded may fail to guide adaptive responses. To facilitate dialog around these issues, the Southeast Climate Consortium and the Florida Climate Institute formed a climate learning network consisting of row crop farmers, agricultural extension specialists, researchers, and climate scientists working in the Southeast US. Regional in scope, the learning network engages researchers and practitioners from Alabama, Georgia, and Florida as partners in adaptation science. This paper describes the ongoing interactions, dialog, and experiential learning among the network&#65533;s diverse participants. We illustrate how participatory tools have been used in a series of workshops to create interactive spaces for knowledge coproduction. For example, historical timelines, climate scenarios, and technology exchanges stimulated discussions about climate-related risk management. We present findings from the workshops related to participants&#65533; perspectives on climate change and adaptation. Finally, we discuss lessons learned that may be applicable to other groups involved in climate education, communication, and stakeholder engagement. We suggest that the thoughtful design of stakeholder engagement processes can become a powerful social tool for improving decision support and strengthening adaptive capacity within rural communities.

Abstract: A critical omission from climate change impact studies on crop yield is the interaction between soil organic carbon (SOC), nitrogen (N) availability, and carbon dioxide (CO2). We used a multimodel ensemble to predict the effects of SOC and N under different scenarios of temperatures and CO2 concentrations on maize (Zea mays L.) and wheat (Triticum aestivum L.) yield in eight sites across the world. We found that including feedbacks from SOC and N losses due to increased temperatures would reduce yields by 13% in wheat and 19% in maize for a 3°C rise temperature with no adaptation practices. These losses correspond to an additional 4.5% (+3°C) when compared to crop yield reductions attributed to temperature increase alone. Future CO2 increase to 540 ppm would partially compensate losses by 80% for both maize and wheat at +3°C, and by 35% for wheat and 20% for maize at +6°C, relative to the baseline CO2 scenario.

Abstract: The hydrological impact of climate change is assessed for 28 watersheds located within the Southeast United States using output from global climate models (GCMs) from the Climate Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) run. Subsequently, the impact of projected change in seasonal streamflow is derived by propagating projected scenarios, generated using changes derived from GCMs and weather generators, through a suite of conceptual hydrological models. Analysis shows that the spread in the magnitude of change in temperature and rainfall for CMIP3 is wider than that for CMIP5. The reduction in the spread among many factors may be attributed to improved physics, model number and resolution, and emission scenarios. The spread in projected change in temperature (precipitation) increases (decreases) from southernmost to northernmost latitude. Hydrological projection with CMIP3 output for the 2070s shows that streamflow decreases for most of the watersheds in spring and summer and increased in fall. In contrast, CMIP5 results show an increase in flow for all seasons except with the high-end scenarios in spring. However, the uncertainty in projections in streamflow is high with model uncertainty dominating emission scenario. The variability in prediction uncertainty among watersheds is partly explained by the variability in wetness index. The probability distribution function for projected seasonal flow for each scenario is markedly wide and therefore reflects that the uncertainty associated with using multiple GCMs from both CMIP3 and CMIP5 experiment is high which makes design and implementation of adaption measure a difficult task.

Abstract: This study investigates the sensitivity of the performance of hydrological models to certain temporal variations of precipitation over the SouthEastern United States (SEUS). Because of observational uncertainty in the estimates of rainfall variability at subdaily scales, the analysis is conducted with two independent rainfall datasets that resolve the diurnal variations. In addition, three hydrological models are used to account for model uncertainty. Results show that the temporal aggregation of subdaily rainfall can translate into a markedly higher volume error in flow simulated by the hydrological models. For the selected watersheds in the SEUS, the volume error is found high (~ 35%) for a 30-day aggregation in some of the selected watersheds. Hydrological models tend to underestimate flow in these watersheds with a decrease in temporal variability in precipitation. Furthermore, diminishing diurnal amplitude by removing subdaily rainfall corresponding to times of climatological daily maximum and minimum has a detrimental effect on the hydrological simulation. This theoretical experiment resulted in the underestimation of flow, with a disproportionate volume error (of as high as 77% in some watersheds). Observations indicate that over the SEUS variations of diurnal variability of rainfall explain a significant fraction of the seasonal variance throughout the year, with especially strong fractional variance explained in the boreal summer season. The results suggest that should diurnal variations of precipitation get modulated either from anthropogenic or natural causes in the SEUS, there will be a significant impact on the streamflow in the watersheds. These conclusions are quite robust since both observational and model uncertainties have been considered in the analysis.

Abstract: We show useful seasonal deterministic and probabilistic prediction skill of streamflow and nutrient loading over watersheds in the Southeastern United States (SEUS) for the winter and spring seasons. The study accounts for forecast uncertainties stemming from the meteorological forcing and hydrological model uncertainty. Multi-model estimation from three hydrological models, each forced with an ensemble of forcing derived by matching observed analogues of forecasted quartile rainfall anomalies from a seasonal climate forecast is used. The attained useful hydrological prediction skill is despite the climate model overestimating rainfall by over 23% over these SEUS watersheds in December–May period. The prediction skill in the month of April and May is deteriorated as compared to the period from December–March (zero lead forecast). A nutrient streamflow rating curve is developed using a log linear tool for this purpose. The skill in the prediction of seasonal nutrient loading is identical to the skill of seasonal streamflow forecast.

Abstract: Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 degrees C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha(-1) for the ensemble at + 6 degrees C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.