Abstract
We explore the possible role of plant–atmosphere feedbacks in accelerating forest expansion using a simple example of forest establishment. We use an unconventional experimental design to simulate an initial forest establishment and the subsequent response of climate and nearby vegetation. We find that the forest’s existence produces favorable nearby growing-season conditions that would promote forest expansion. Specifically, we consider a hypothetical region of forest expansion in modern Alaska. We find that the forest acts as a source of heat and moisture for plants to the west, leading them to experience earlier springtime temperatures, snowmelt, and growth. Summertime cooling and cloud formation over the forest also drive a circulation change that reduces summertime cloud cover south of the forest, increasing solar radiation reaching plants there and driving warming. By isolating these vegetation–atmosphere interactions as the mechanisms of increased growth, we demonstrate the potential for forest expansion to be accelerated in a way that has not been highlighted before. These simulations illuminate two separate mechanisms that lead to increased plant growth nearby: 1) springtime heat advection and 2) summertime cloud feedbacks and circulation changes; both have implications for our understanding of past changes in forest cover and the predictability of biophysical impacts from afforestation projects and climate change–driven forest-cover changes. By examining these feedbacks, we seek to gain a more comprehensive understanding of past and potential future land–atmosphere interactions.
Significance Statement
This study investigates whether the emergence of a high-latitude forest could influence the way water and energy are exchanged between the land and atmosphere in a way that impacts nearby growing conditions and subsequent forest expansion. We use a computer model to simulate a climate with and without forest establishment in the high latitudes and test the response of plants surrounding the forest to the two different climates. We find that a forest is indeed able to spur neighboring plant growth by modifying regional climate and producing more favorable growing conditions for surrounding vegetation. Specifically, forest establishment can bring better growing conditions to plants adjacent to it by warming the air and altering nearby circulation and cloud cover.
Abstract
We explore the possible role of plant–atmosphere feedbacks in accelerating forest expansion using a simple example of forest establishment. We use an unconventional experimental design to simulate an initial forest establishment and the subsequent response of climate and nearby vegetation. We find that the forest’s existence produces favorable nearby growing-season conditions that would promote forest expansion. Specifically, we consider a hypothetical region of forest expansion in modern Alaska. We find that the forest acts as a source of heat and moisture for plants to the west, leading them to experience earlier springtime temperatures, snowmelt, and growth. Summertime cooling and cloud formation over the forest also drive a circulation change that reduces summertime cloud cover south of the forest, increasing solar radiation reaching plants there and driving warming. By isolating these vegetation–atmosphere interactions as the mechanisms of increased growth, we demonstrate the potential for forest expansion to be accelerated in a way that has not been highlighted before. These simulations illuminate two separate mechanisms that lead to increased plant growth nearby: 1) springtime heat advection and 2) summertime cloud feedbacks and circulation changes; both have implications for our understanding of past changes in forest cover and the predictability of biophysical impacts from afforestation projects and climate change–driven forest-cover changes. By examining these feedbacks, we seek to gain a more comprehensive understanding of past and potential future land–atmosphere interactions.
Significance Statement
This study investigates whether the emergence of a high-latitude forest could influence the way water and energy are exchanged between the land and atmosphere in a way that impacts nearby growing conditions and subsequent forest expansion. We use a computer model to simulate a climate with and without forest establishment in the high latitudes and test the response of plants surrounding the forest to the two different climates. We find that a forest is indeed able to spur neighboring plant growth by modifying regional climate and producing more favorable growing conditions for surrounding vegetation. Specifically, forest establishment can bring better growing conditions to plants adjacent to it by warming the air and altering nearby circulation and cloud cover.
Abstract
Organized systems of deep convective clouds are often associated with high-impact weather and changes in such systems may have implications for climate sensitivity. This has motivated the derivation of many organization indices that attempt to measure the level of deep convective aggregation in models and observations. Here we conduct a comprehensive review of existing methodologies and highlight some of their relative drawbacks, such as only measuring organization in a relative sense, being biased towards particular spatial scales, or being very sensitive to the details of the calculation algorithm. One widely used metric, I org, uses statistics of nearest-neighbor distances between convective storms to address the first of these concerns, but we show here that it is insensitive to organization beyond the β-mesoscale and very contingent on the details of the implementation. We thus introduce a new and complementary metric, L org, based on all-pair convective storm distances, which is also an absolute metric that can discern regular, random and clustered cloud scenes. It is linearly sensitive to spatial scale in most applications and robust to the implementation methodology. We also derive a discrete form suited to gridded data and provide corrections to account for cyclic boundary conditions and finite, open boundary domains of non-equal aspect ratios. We demonstrate the use of the metric with idealized synthetic configurations, as well as model output and satellite rainfall retrievals in the tropics. We claim that this new metric usefully supplements the existing family of indices that can help understand convective organization across spatial scales.
Abstract
Organized systems of deep convective clouds are often associated with high-impact weather and changes in such systems may have implications for climate sensitivity. This has motivated the derivation of many organization indices that attempt to measure the level of deep convective aggregation in models and observations. Here we conduct a comprehensive review of existing methodologies and highlight some of their relative drawbacks, such as only measuring organization in a relative sense, being biased towards particular spatial scales, or being very sensitive to the details of the calculation algorithm. One widely used metric, I org, uses statistics of nearest-neighbor distances between convective storms to address the first of these concerns, but we show here that it is insensitive to organization beyond the β-mesoscale and very contingent on the details of the implementation. We thus introduce a new and complementary metric, L org, based on all-pair convective storm distances, which is also an absolute metric that can discern regular, random and clustered cloud scenes. It is linearly sensitive to spatial scale in most applications and robust to the implementation methodology. We also derive a discrete form suited to gridded data and provide corrections to account for cyclic boundary conditions and finite, open boundary domains of non-equal aspect ratios. We demonstrate the use of the metric with idealized synthetic configurations, as well as model output and satellite rainfall retrievals in the tropics. We claim that this new metric usefully supplements the existing family of indices that can help understand convective organization across spatial scales.
Abstract
The Global Positioning System dropwindsonde has provided thousands of high-resolution kinematic and thermodynamic soundings in and around tropical cyclones (TCs) since 1997. These data have revolutionized the understanding of TC structure, improved forecasts, and validated observations from remote-sensing platforms. About 400 peer-reviewed studies on TCs using these data have been published to date. This paper reviews the history of dropwindsonde observations, changes to dropwindsonde technology since it was first used in TCs in 1982, and how the data have improved forecasting and changed our understanding of TCs.
Abstract
The Global Positioning System dropwindsonde has provided thousands of high-resolution kinematic and thermodynamic soundings in and around tropical cyclones (TCs) since 1997. These data have revolutionized the understanding of TC structure, improved forecasts, and validated observations from remote-sensing platforms. About 400 peer-reviewed studies on TCs using these data have been published to date. This paper reviews the history of dropwindsonde observations, changes to dropwindsonde technology since it was first used in TCs in 1982, and how the data have improved forecasting and changed our understanding of TCs.
Abstract
To address critical gaps identified by the National Academies of Sciences, Engineering and Medicine in the current Earth system observation strategy, the 2017-2027 Decadal Survey for Earth Science and Applications from Space recommended incubating concepts for future targeted observables including the atmospheric planetary boundary layer (PBL). A subsequent NASA PBL Incubation Study Team Report identified measurement requirements and activities for advancing the maturity of the technologies applicable to the PBL targeted observables and their associated science and applications priorities. While the PBL is the critical layer where humans live and surface energy, moisture, and mass exchanges drive the Earth system, it is also the farthest and most inaccessible layer for spaceborne instruments. Here we document a PBL retrieval Observing System Simulation Experiment (OSSE) framework suitable for assessing existing and new measurement techniques and determining their accuracy and improvements needed for addressing the elevated Decadal Survey requirements. In particular, the benefits of Large-Eddy Simulation (LES) are emphasized as a key source of high-resolution synthetic observations for key PBL regimes: from the tropics, through sub-tropics and mid-latitudes, to subpolar and polar regions. The potential of LES-based PBL retrieval OSSEs is explored using six instrument simulators: global navigation satellite system-radio occultation, differential absorption radar, visible to shortwave infrared spectrometer, infrared sounder, multi-angle imaging radio spectrometer, and microwave sounder. The crucial role of LES in PBL retrieval OSSEs and some perspectives for instrument developments are discussed.
Abstract
To address critical gaps identified by the National Academies of Sciences, Engineering and Medicine in the current Earth system observation strategy, the 2017-2027 Decadal Survey for Earth Science and Applications from Space recommended incubating concepts for future targeted observables including the atmospheric planetary boundary layer (PBL). A subsequent NASA PBL Incubation Study Team Report identified measurement requirements and activities for advancing the maturity of the technologies applicable to the PBL targeted observables and their associated science and applications priorities. While the PBL is the critical layer where humans live and surface energy, moisture, and mass exchanges drive the Earth system, it is also the farthest and most inaccessible layer for spaceborne instruments. Here we document a PBL retrieval Observing System Simulation Experiment (OSSE) framework suitable for assessing existing and new measurement techniques and determining their accuracy and improvements needed for addressing the elevated Decadal Survey requirements. In particular, the benefits of Large-Eddy Simulation (LES) are emphasized as a key source of high-resolution synthetic observations for key PBL regimes: from the tropics, through sub-tropics and mid-latitudes, to subpolar and polar regions. The potential of LES-based PBL retrieval OSSEs is explored using six instrument simulators: global navigation satellite system-radio occultation, differential absorption radar, visible to shortwave infrared spectrometer, infrared sounder, multi-angle imaging radio spectrometer, and microwave sounder. The crucial role of LES in PBL retrieval OSSEs and some perspectives for instrument developments are discussed.
Abstract
The Marshall Fire on 30 December 2021 became the most destructive wildfire cost-wise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly eleven hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the mid-level inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a high-wind warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a county-wide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts – important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
Abstract
The Marshall Fire on 30 December 2021 became the most destructive wildfire cost-wise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly eleven hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the mid-level inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a high-wind warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a county-wide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts – important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
Abstract
Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada Mountains. Rain-on-snow (ROS) events are the common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981-2019. Observations across a 500 m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a 1-dimensional, physically-based snow model, initiated with the Richards Equation and calibrated with co-located snow pillow observations. We compare observed ‘historical’ and ‘scenario’ ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include: cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g/cm 3) and colder (>0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding.
Abstract
Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada Mountains. Rain-on-snow (ROS) events are the common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981-2019. Observations across a 500 m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a 1-dimensional, physically-based snow model, initiated with the Richards Equation and calibrated with co-located snow pillow observations. We compare observed ‘historical’ and ‘scenario’ ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include: cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g/cm 3) and colder (>0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding.
Abstract
Fire emissions from the Maritime Continent (MC) over the western tropical Pacific are strongly influenced by El Niño–Southern Oscillation (ENSO), posing various climate effect to the Earth system. In this study, we show that the historical biomass burning emissions of black carbon (BCbb) aerosol in the dry season from the MC are strengthened in El Niño years due to the dry conditions. The Eastern-Pacific type of El Niño exerts a stronger modulation in BCbb emissions over the MC region than the Central-Pacific type of El Niño. Based on simulations using the fully coupled Community Earth System Model (CESM), the impacts of increased BCbb emissions on ENSO variability and frequency are also investigated in this study. With BCbb emissions from the MC scaled up by a factor of 10, which enables the identification of climate response from the internal variability, the increased BCbb heats the local atmosphere and changes land-sea thermal contrast, which suppresses the westward transport of the eastern Pacific surface water. It leads to an increase of sea surface temperature in the eastern tropical Pacific, which further enhances ENSO variability and increases the frequency of extreme El Niño and La Niña events. This study highlights the potential role of BCbb emissions on extreme ENSO frequency and this role may be increasingly important in the warming future with higher wildfire risks.
Abstract
Fire emissions from the Maritime Continent (MC) over the western tropical Pacific are strongly influenced by El Niño–Southern Oscillation (ENSO), posing various climate effect to the Earth system. In this study, we show that the historical biomass burning emissions of black carbon (BCbb) aerosol in the dry season from the MC are strengthened in El Niño years due to the dry conditions. The Eastern-Pacific type of El Niño exerts a stronger modulation in BCbb emissions over the MC region than the Central-Pacific type of El Niño. Based on simulations using the fully coupled Community Earth System Model (CESM), the impacts of increased BCbb emissions on ENSO variability and frequency are also investigated in this study. With BCbb emissions from the MC scaled up by a factor of 10, which enables the identification of climate response from the internal variability, the increased BCbb heats the local atmosphere and changes land-sea thermal contrast, which suppresses the westward transport of the eastern Pacific surface water. It leads to an increase of sea surface temperature in the eastern tropical Pacific, which further enhances ENSO variability and increases the frequency of extreme El Niño and La Niña events. This study highlights the potential role of BCbb emissions on extreme ENSO frequency and this role may be increasingly important in the warming future with higher wildfire risks.
