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Agricultural intensification in India has increased nitrogen pollution, leading to water quality impairments. The fate of reactive nitrogen applied to the land is largely unknown, however. Long-term records of riverine nitrogen fluxes are nonexistent and drivers of variability remain unexamined, limiting the development of nitrogen management strategies. Here, we leverage dissolved inorganic nitrogen (DIN) and discharge data to characterize the seasonal, annual, and regional variability of DIN fluxes and their drivers for seven major river basins from 1981 to 2014. We find large seasonal and interannual variability in nitrogen runoff, with 68% to 94% of DIN fluxes occurring in June through October and with the coefficient of variation across years ranging from 44% to 93% for individual basins. This variability is primarily explained by variability in precipitation, with year- and basin-specific annual precipitation explaining 52% of the combined regional and interannual variability. We find little correlation with rising fertilizer application rates in five of the seven basins, implying that agricultural intensification has thus far primarily impacted groundwater and atmospheric emissions rather than riverine runoff. These findings suggest that riverine nitrogen runoff in India is highly sensitive to projected future increases in precipitation and intensification of the seasonal monsoon, while the impact of projected continued land use intensification is highly uncertain.

Climate change is expected to impact the severity of harmful algal blooms in lakes and reservoirs through a number of mechanisms related to the influence of warming temperatures and changes to precipitation patterns. Evidence on the prevalence of individual mechanisms is lacking, however, with knowledge of many mechanisms restricted to studies of individual or small subsets of lakes. Here, we leverage over twelve hundred summertime lake observations from across the continental U.S. to explore evidence for the hypothesized risks from climate change attributable to specific mechanisms. Using a statistical model selection approach, we examine associations between temperature and precipitation variables and indicators of total phytoplankton abundance, species dominance, and toxicity. We find evidence in support of the hypotheses that summer temperatures drive total abundance, that the length of the summer drives cyanobacterial abundance, and that increased temperatures may reduce the observed toxicity of blooms in some cases. We find that nutrient concentrations are also likely to be impacted by lake warming, as increased temperatures are robustly associated with increased total phosphorus concentrations. Evidence for the impact of precipitation is mixed, however, as there is evidence to support that increased nutrient runoff from precipitation could support blooms but also that nutrient concentrations could be reduced through greater flushing due to precipitation. While statistical associations are not definitive evidence of formal mechanistic links, the geographic scale of the results is useful for identifying hypothesized mechanisms that are widespread across the continental U.S., and therefore for informing understanding of the influence of climate change.

Freshwater blooms of phytoplankton affect public health and ecosystem services globally1,2. Harmful effects of such blooms occur when the intensity of a bloom is too high, or when toxin-producing phytoplankton species are present. Freshwater blooms result in economic losses of more than US$4 billion annually in the United States alone, primarily from harm to aquatic food production, recreation and tourism, and drinking-water supplies3. Studies that document bloom conditions in lakes have either focused only on individual or regional subsets of lakes4–6, or have been limited by a lack of long-term observations7–9. Here we use three decades of high-resolution Landsat 5 satellite imagery to investigate long-term trends in intense summertime near-surface phytoplankton blooms for 71 large lakes globally. We find that peak summertime bloom intensity has increased in most (68 per cent) of the lakes studied, revealing a global exacerbation of bloom conditions. Lakes that have experienced a significant (P< 0.1) decrease in bloom intensity are rare (8 per cent). The reason behind the increase in phytoplankton bloom intensity remains unclear, however, as temporal trends do not track consistently with temperature, precipitation, fertilizer-use trends or other previously hypothesized drivers. We do find, however, that lakes with a decrease in bloom intensity warmed less compared to other lakes, suggesting that lake warming may already be counteracting management efforts to ameliorate eutrophication10,11. Our findings support calls for water quality management efforts to better account for the interactions between climate change and local hydrological conditions12,13.

Clarifying how increased atmospheric CO2concentration (eCO2) contributes to accelerated land carbon sequestration remains important since this process is the largest negative feedback in the coupled carbon–climate system. Here, we constrain the sensitivity of the terrestrial carbon sink to eCO2over the temperate Northern Hemisphere for the past five decades, using 12 terrestrial ecosystem models and data from seven CO2enrichment experiments. This constraint uses the heuristic finding that the northern temperate carbon sink sensitivity to eCO2is linearly related to the site-scale sensitivity across the models. The emerging data-constrained eCO2sensitivity is 0.64 ± 0.28 PgC yr−1per hundred ppm of eCO2. Extrapolating worldwide, this northern temperate sensitivity projects the global terrestrial carbon sink to increase by 3.5 ± 1.9 PgC yr−1for an increase in CO2of 100 ppm. This value suggests that CO2fertilization alone explains most of the observed increase in global land carbon sink since the 1960s. More CO2enrichment experiments, particularly in boreal, arctic and tropical ecosystems, are required to explain further the responsible processes.

Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg(18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems. A GPP‐traceability framework is established to diagnose the uncertainty sources of modeled GPPLarge intermodel differences of modeled GPP result from their different representation of vegetation functional propertiesPositive bias in simulated GPP over the East Asian monsoon region could be attributed to the higher simulated CUE and SLA comparing with observations

Increases in nitrogen loading over the past several decades have led to widespread water quality impairments across the U.S. Elevated awareness of the influence of climate variability on nitrogen loading has led to several studies investigating future climate change impacts on water quality. However,  it remains unclear whether long-term climate impacts can already be observed in the historical record. Here, we quantify long-term trends in total nitrogen loading over the period 1987–2012 across the contiguous U.S. and attribute these trends to long-term changes in nitrogen inputs and climatic variables. We find that annual precipitation, extreme springtime precipitation, and springtime temperature are key drivers of trends in historical loading in most regions. These decadal climate trends have either  amplified or offset loading trends expected from nitrogen inputs alone. We also find that rising temperatures have been insufficient to offset precipitation-induced loading increases, suggesting that future increases in temperature under climate change may have limited potential to counteract loading increases expected as a result of anticipated changes in precipitation. This work demonstrates the important role of decadal climate variability in long-term nitrogen loading, emphasizing the need to consider climate change risks when designing and monitoring nutrient reduction programs.

Recent studies have shown the promise of remotely sensed solar‐induced chlorophyll fluorescence (SIF) in informing terrestrial carbon exchange, but analyses have been limited to either plot level (~1 km2) or hemispheric/global (~108km2) scales due to the lack of a direct measure of carbon exchange at intermediate scales. Here we use a network of atmospheric CO2observations over North America to explore the value of SIF for informing net ecosystem exchange (NEE) at regional scales. We find that SIF explains space‐time NEE patterns at regional (~100 km2) scales better than a variety of other vegetation and climate indicators. We further show that incorporating SIF into an atmospheric inversion leads to a spatial redistribution of NEE estimates over North America, with more uptake attributed to agricultural regions and less to needleleaf forests. Our results highlight the synergy of ground‐based and spaceborne carbon cycle observations. Solar‐induced fluorescence explains regional net ecosystem exchange over North America better than existing data sets and mechanistic modelsInformation provided by SIF is particularly beneficial for explaining the NEE signal in croplands and needleleaf forestsSIF‐informed inverse estimates of NEE sinks show consistent growing season shift from needleleaf forests to croplands in North America

Water use efficiency (WUE), defined as the ratio of gross primary productivity and evapotranspiration at the ecosystem scale, is a critical variable linking the carbon and water cycles. Incorporating a dependency on vapor pressure deficit, apparent underlying WUE (uWUE) provides a better indicator of how terrestrial ecosystems respond to environmental changes than other WUE formulations. Here we used 20th century simulations from four terrestrial biosphere models to develop a novel variance decomposition method. With this method, we attributed variations in apparent uWUE to both the trend and interannual variation of environmental drivers. The secular increase in atmospheric CO2explained a clear majority of total variation (66 ± 32%: mean ± one standard deviation), followed by positive trends in nitrogen deposition and climate, as well as a negative trend in land use change. In contrast, interannual variation was mostly driven by interannual climate variability. To analyze the mechanism of the CO2effect, we partitioned the apparent uWUE into the transpiration ratio (transpiration over evapotranspiration) and potential uWUE. The relative increase in potential uWUE parallels that of CO2, but this direct CO2effect was offset by 20 ± 4% by changes in ecosystem structure, that is, leaf area index for different vegetation types. However, the decrease in transpiration due to stomatal closure with rising CO2was reduced by 84% by an increase in leaf area index, resulting in small changes in the transpiration ratio. CO2concentration thus plays a dominant role in driving apparent uWUE variations over time, but its role differs for the two constituent components: potential uWUE and transpiration. A new method was proposed to attribute the variation in uWUEa to four environmental driversAtmospheric CO2determines the trend in uWUEa, while interannual variability of climate determines that of uWUEaThe physiological CO2effect on uWUEp was offset by 20% and that on transpiration by 84% with changes in LAI

Drought, a recurring phenomenon with major impacts on both human and natural systems, is the most widespread climatic extreme that negatively affects the land carbon sink. Although twentieth-century trends in drought regimes are ambiguous, across many regions more frequent and severe droughts are expected in the twenty-first century. Recovery time—how long an ecosystem requires to revert to its pre-drought functional state—is a critical metric of drought impact. Yet the factors influencing drought recovery and its spatiotemporal patterns at the global scale are largely unknown. Here we analyse three independent datasets of gross primary productivity and show that, across diverse ecosystems, drought recovery times are strongly associated with climate and carbon cycle dynamics, with biodiversity and CO2fertilization as secondary factors. Our analysis also provides two key insights into the spatiotemporal patterns of drought recovery time: first, that recovery is longest in the tropics and high northern latitudes (both vulnerable areas of Earth’s climate system) and second, that drought impacts (assessed using the area of ecosystems actively recovering and time to recovery) have increased over the twentieth century. If droughts become more frequent, as expected, the time between droughts may become shorter than drought recovery time, leading to permanently damaged ecosystems and widespread degradation of the land carbon sink.

Warming temperatures in the Northern Hemisphere have enhanced terrestrial productivity. Despite the warming trend, North America has experienced more frequent and more intense cold weather events during winters and springs. These events have been linked to anomalous Arctic warming since 1990, and may affect terrestrial processes. Here we analyse multiple observation data sets and numerical model simulations to evaluate links between Arctic temperatures and primary productivity in North America. We find that positive springtime temperature anomalies in the Arctic have led to negative anomalies in gross primary productivity over most of North America during the last three decades, which amount to a net productivity decline of 0.31 PgC yr−1across the continent. This decline is mainly explained by two factors: severe cold conditions in northern North America and lower precipitation in the South Central United States. In addition, United States crop-yield data reveal that during years experiencing anomalous warming in the Arctic, yields declined by approximately 1 to 4% on average, with individual states experiencing declines of up to 20%. We conclude that the strengthening of Arctic warming anomalies in the past decades has remotely reduced productivity over North America.

Methane (CH4) fluxes from Alaska and other arctic regions may be sensitive to thawing permafrost and future climate change, but estimates of both current and future fluxes from the region are uncertain. This study estimates CH4fluxes across Alaska for 2012–2014 using aircraft observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and a geostatistical inverse model (GIM). We find that a simple flux model based on a daily soil temperature map and a static map of wetland extent reproduces the atmospheric CH4observations at the statewide, multiyear scale more effectively than global-scale process-based models. This result points to a simple and effective way of representing CH4fluxes across Alaska. It further suggests that process-based models can improve their representation of key processes and that more complex processes included in these models cannot be evaluated given the information content of available atmospheric CH4observations. In addition, we find that CH4emissions from the North Slope of Alaska account for 24% of the total statewide flux of 1.74 ± 0.26 Tg CH4(for May–October). Global-scale process models only attribute an average of 3% of the total flux to this region. This mismatch occurs for two reasons: process models likely underestimate wetland extent in regions without visible surface water, and these models prematurely shut down CH4fluxes at soil temperatures near 0°C. Lastly, we find that the seasonality of CH4fluxes varied during 2012–2014 but that total emissions did not differ significantly among years, despite substantial differences in soil temperature and precipitation. A simple model of soil temperature and wetland distribution can reproduce patterns in atmospheric CH4observationsThe largest CH4fluxes in Alaska occur in lowland tundra—in the southwest (e.g., Yukon-Kuskokwim Delta) and North SlopeWe do not find evidence for large year-to-year variability in total CH4fluxes from Alaska

Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature‐based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr−1, respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time‐varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr−1during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models. Large uncertainty exists in estimates of terrestrial NPP and NBP in ChinaMethodological differences greatly contribute to the uncertainty in NPP and NBPUncertainty in the interannual pattern of NBP is greater than that of NPP

The terrestrial biosphere can release or absorb the greenhouse gases, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), and therefore has an important role in regulating atmospheric composition and climate. Anthropogenic activities such as land-use change, agriculture and waste management have altered terrestrial biogenic greenhouse gas fluxes, and the resulting increases in methane and nitrous oxide emissions in particular can contribute to climate change. The terrestrial biogenic fluxes of individual greenhouse gases have been studied extensively, but the net biogenic greenhouse gas balance resulting from anthropogenic activities and its effect on the climate system remains uncertain. Here we use bottom-up (inventory, statistical extrapolation of local flux measurements, and process-based modelling) and top-down (atmospheric inversions) approaches to quantify the global net biogenic greenhouse gas balance between 1981 and 2010 resulting from anthropogenic activities and its effect on the climate system. We find that the cumulative warming capacity of concurrent biogenic methane and nitrous oxide emissions is a factor of about two larger than the cooling effect resulting from the global land carbon dioxide uptake from 2001 to 2010. This results in a net positive cumulative impact of the three greenhouse gases on the planetary energy budget, with a best estimate (in petagrams of CO2equivalent per year) of 3.9 ± 3.8 (top down) and 5.4 ± 4.8 (bottom up) based on the GWP100 metric (global warming potential on a 100-year time horizon). Our findings suggest that a reduction in agricultural methane and nitrous oxide emissions, particularly in Southern Asia, may help mitigate climate change.

The year 2015 marks the 50th anniversary of Water Resources Research (WRR), which was founded in 1965. More than 15,000 papers have been published in WRR since its inception, and these papers have been cited more than 430,000 times. The history of hydrology and the water sciences are also reflected in WRR, which has served as a premier publication outlet and instigator of scientific growth over the last 50 years. The legacy of WRR provides a strong scientific foundation for the hydrology community to rise to the challenges of sustainable water resources management in a future where dramatic environmental change and increasing human population are expected to stress the world's water resources from local to global scales. History of Water Resources ResearchResearch trends in hydrology over the last 50 yearsSome bibliometrics of Water Resources Research

Multimodel ensembles (MME) are commonplace in Earth system modeling. Here we perform MME integration using a 10‐member ensemble of terrestrial biosphere models (TBMs) from the Multiscale synthesis and Terrestrial Model Intercomparison Project (MsTMIP). We contrast optimal (skill based for present‐day carbon cycling) versus naïve (“one model‐one vote”) integration. MsTMIP optimal and naïve mean land sink strength estimates (−1.16 versus −1.15 Pg C per annum respectively) are statistically indistinguishable. This holds also for grid cell values and extends to gross uptake, biomass, and net ecosystem productivity. TBM skill is similarly indistinguishable. The added complexity of skill‐based integration does not materially change MME values. This suggests that carbon metabolism has predictability limits and/or that all models and references are misspecified. Resolving this issue requires addressing specific uncertainty types (initial conditions, structure, and references) and a change in model development paradigms currently dominant in the TBM community. Naive (simple mean) and skill‐based multi‐model ensemble integration are indistinguishableThis holds at the grid cell level and globally for land sink strength and reference variablesCarbon metabolism has predictability limits and/or models/references are misspecified

Continental North America has been found to be a carbon (C) sink over recent decades by multiple studies employing a variety of estimation approaches. However, several key questions and uncertainties remain with these assessments. Here we used results from an ensemble of 19 state‐of‐the‐art dynamic global vegetation models from the TRENDYv9 project to improve these estimates and study the drivers of its interannual variability. Our results show that North America has been a C sink with a magnitude of 0.37 ± 0.38 (mean and one standard deviation) PgC year−1for the period 2000–2019 (0.31 and 0.44 PgC year−1in each decade); split into 0.18 ± 0.12 PgC year−1in Canada (0.15 and 0.20), 0.16 ± 0.17 in the United States (0.14 and 0.17), 0.02 ± 0.05 PgC year−1in Mexico (0.02 and 0.02) and 0.01 ± 0.02 in Central America and the Caribbean (0.01 and 0.01). About 57% of the new C assimilated by terrestrial ecosystems is allocated into vegetation, 30% into soils, and 13% into litter. Losses of C due to fire account for 41% of the interannual variability of the mean net biome productivity for all North America in the model ensemble. Finally, we show that drought years (e.g., 2002) have the potential to shift the region to a small net C source in the simulations (−0.02 ± 0.46 PgC year−1). Our results highlight the importance of identifying the major drivers of the interannual variability of the continental‐scale land C cycle along with the spatial distribution of local sink‐source dynamics. In recent decades terrestrial ecosystems in North America have absorbed more carbon (C) from the atmosphere than they have released, thus acting as a net land C sink. Nonetheless, several gaps remain in our understanding of how the newly assimilated C is partitioned among the various components of the land ecosystem, and about the impact of recent fires and extreme climatic events on the C stored in vegetation and the soil. In this study, we employed 19 state‐of‐the‐art global dynamic vegetation models to advance our understanding of the land C dynamics over North America over the period 2000–2019. Our results suggest that 57% of the newly assimilated C over this period went into the vegetation, 30% to the soils, and the rest as litter. These new land C gains were found mostly over the temperate and boreal forest regions, which together accounted for 81% of the entire North American sink. Finally, we show that fire and drought are closely related to the year‐to‐year variability of the sink magnitude, and during extreme conditions (e.g., during 2002) they have the potential to reverse the land into a C source to the atmosphere. Land C sink dynamics for North America were evaluated using 19 state‐of‐the‐art Dynamic Global Vegetation Models during 2000–2019North America has been a net C sink with a magnitude of 0.37 ± 0.38 PgC year−1and an increasing trend over timeWe identify different spatial and temporal drivers of the variability in land C fluxes across the region, including fire and drought Land C sink dynamics for North America were evaluated using 19 state‐of‐the‐art Dynamic Global Vegetation Models during 2000–2019 North America has been a net C sink with a magnitude of 0.37 ± 0.38 PgC year−1and an increasing trend over time We identify different spatial and temporal drivers of the variability in land C fluxes across the region, including fire and drought

The future carbon balance of boreal ecosystems under increasing temperatures is highly uncertain. In particular, the net effects of a longer growing season versus enhanced respiration are poorly understood. Here, we use a geostatistical inverse model from 2012 to 2014 to determine temperature sensitivity in Alaskan biomes throughout the growing season, in order to identify the relative effects of these competing phenomena. We find that temperature explains a large portion of the disparities in autumn carbon flux between 2013 and 2014. Boreal forests experienced a growing season extension during the warm October of 2013 that offset increased respiration into autumn in years with high temperatures. In contrast, increased temperatures in the tundra and shrublands led to a large respiration signal during October 2013, producing a greater net carbon release. These results suggest a greater vulnerability of Alaskan tundra and shrubland carbon stocks compared to boreal forest carbon stocks under warming. Increasing atmospheric concentrations of greenhouse gases are driving climate change, which is happening particularly rapidly near the poles of the Earth. This rapid warming may help plants in these high‐latitude regions take up more carbon through photosynthesis, which could decrease the amount of carbon dioxide (CO2) in the atmosphere, slowing climate change. However, rapid warming is also speeding up soil microbial activity and other processes that release more CO2into the atmosphere at high latitudes, possibly accelerating climate change. Therefore, the balance of these different processes will determine the net impact of high‐latitude ecosystems on climate change. We assess how much warming and associated conditions influence the total CO2exchange across Alaska. We find that during our study period, warming was very important for autumn CO2exchange. Forests in Alaska continued their photosynthesis later into autumn during the warmest year of our study period, offsetting enhanced CO2release from these regions. In contrast, under the same conditions, areas covered by shrubs and grasses simply lost large amounts of carbon. These results suggest that under warming, high‐latitude grasslands and shrublands are particularly susceptible to releasing large amounts of CO2, which could potentially accelerate climate change. Autumn warming is associated with a growing season extension in the boreal forests in the interior of AlaskaThese same conditions are associated with a strong release of carbon from shrublands and some tundra regions in AlaskaShrubland and tundra regions of Alaska may be more vulnerable to net carbon losses because of a lack of a growing season extension Autumn warming is associated with a growing season extension in the boreal forests in the interior of Alaska These same conditions are associated with a strong release of carbon from shrublands and some tundra regions in Alaska Shrubland and tundra regions of Alaska may be more vulnerable to net carbon losses because of a lack of a growing season extension

While substantial attention has been paid to the effects of both global climate oscillations and local meteorological conditions on the interannual variability of ecosystem carbon exchange, the relationship between the interannual variability of synoptic meteorology and ecosystem carbon exchange has not been well studied. Here we use a clustering algorithm to identify a summertime cyclonic precipitation system northwest of the Great Lakes to determine (a) the association at a daily scale between the occurrence of this system and the local meteorology and net ecosystem exchange at three Great Lakes region forested eddy covariance sites and (b) the association between the seasonal prevalence of this system and the summertime net ecosystem exchange of these sites. We find that temperature, in addition to precipitation and cloud cover, is an important explanatory factor for the suppression of net ecosystem productivity that occurs during these cyclonic events in this region. In addition, the prevalence of this cyclonic system can explain a significant proportion of the interannual variability in summertime forest ecosystem exchange in this region. This explanatory power is not due to a simple accumulation of low‐productivity days that cooccur with this meteorological event, but rather a broader association between the frequency of these events and several aspects of prevailing seasonal conditions. This work demonstrates the usefulness of conceptualizing meteorology in terms of synoptic systems for explaining the interannual variability of regional carbon fluxes. Ecosystems exchange large amounts of carbon dioxide (CO2) with the atmosphere. This rate of exchange is influenced by meteorological and climatological conditions. Because CO2is a major greenhouse gas, we need to determine the association between climate and ecosystem‐atmosphere CO2exchange in order to understand potential climate feedbacks. Here we hypothesized that continental‐scale atmospheric systems would be more informative than individual local meteorological variables for explaining the variability of regional carbon exchange, because these larger‐scale systems concurrently indicate the state of several meteorological variables at various timescales, producing additive or interactive effects. We identified a particular summer storm system that leads to summertime rainfall and increased temperatures over and to the northwest of the Great Lakes. We found that the occurrence of this storm system is associated with weaker ecosystem carbon uptake at forests in the Great Lakes region. The widespread increase in temperature appears to contribute to this inhibition. A dampened seasonal cycle of carbon uptake is also associated with a high frequency of occurrence of this system. This work shows that large‐scale weather patterns can help us understand how the ecosystem‐atmosphere CO2exchange responds to climate. Anomalously high temperatures, in addition to cloud cover, contribute to daily‐scale suppression of net ecosystem productivity at forests in the Great Lakes region during nearby cyclonic precipitation events during the growing seasonForests in the Great Lakes region experience dampened growing season net ecosystem productivity during years in which this cyclonic system occurs frequentlyPatterns of synoptic meteorology help inform regional net ecosystem productivity

Observations show an increasing amplitude in the seasonal cycle of CO2(ASC) north of 45°N of 56 ± 9.8% over the last 50 years and an increase in vegetation greenness of 7.5–15% in high northern latitudes since the 1980s. However, the causes of these changes remain uncertain. Historical simulations from terrestrial biosphere models in the Multiscale Synthesis and Terrestrial Model Intercomparison Project are compared to the ASC and greenness observations, using the TM3 atmospheric transport model to translate surface fluxes into CO2concentrations. We find that the modeled change in ASC is too small but the mean greening trend is generally captured. Modeled increases in greenness are primarily driven by warming, whereas ASC changes are primarily driven by increasing CO2. We suggest that increases in ecosystem‐scale light use efficiency (LUE) have contributed to the observed ASC increase but are underestimated by current models. We highlight potential mechanisms that could increase modeled LUE. Current terrestrial biosphere models underestimate northern CO2seasonal cycle amplitude increaseModels capture observed greening trends and therefore increased uptake of light by ecosystemsReconciling these suggests an increase in ecosystem light use efficiency larger than models simulate