There is growing interest in the study of causal methods in the Earth sciences. However, most applications have focused on causal discovery, i.e. inferring the causal relationships and causal structure from data. This paper instead examines causality through the lens of causal inference and how expert-defined causal graphs, a fundamental from causal theory, can be used to clarify assumptions, identify tractable problems, and aid interpretation of results and their causality in Earth science research. We apply causal theory to generic graphs of the Earth system to identify where causal inference may be most tractable and useful to address problems in Earth Science, and avoid potentially incorrect conclusions. Specifically, causal inference may be useful when: (1) the effect of interest is only causally affected by the observed portion of the state space; or: (2) the cause of interest can be assumed to be independent of the evolution of the system's state; or: (3) the state space of the system is reconstructable from lagged observations of the system. However, we also highlight through examples how causal graphs can be used to explicitly define and communicate assumptions and hypotheses, and help to structure analyses, even if causal inference is ultimately challenging given the data availability, limitations and uncertainties.

Satellite data provide crucial information for those places lacking precipitation observations from ground-based sensors, especially over oceans, mountain regions, or developing countries. This is the case over much of South America, including Chile, a country with complex topography that has limited long-term precipitation records and high-elevation data, and no operational weather radars. This study focuses on investigating the skill of the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrieval for GPM (IMERG: version 6) quantitative precipitation estimation (QPE). IMERG is assessed against ground-based observations from two field campaigns that took place near 36°S: The Chilean Coastal Orographic Precipitation Experiment (CCOPE; winter 2015), which collected data over the coastal mountain range, and The Chilean Orographic and Mesoscale Precipitation Study (ChOMPS; winter 2016), which collected observations in a transect from the coast to the Andes. To characterize how IMERG performance depends on microphysical regime, we used data from profiling radars and rain gauge measurements to classify rainfall into regimes including “ice-initiated rain” and “warm rain”, characterized by the presence or absence of a well-defined melting layer respectively. Rain gauge data was used to evaluate performance of IMERG QPE overall and for these two regimes. IMERG depicts the general spatial pattern of observed orographic enhancement but highly underestimates the magnitude of this enhancement. At higher elevations during CCOPE, IMERG underestimated the total amount of rainfall by 50%, while during ChOMPS the underestimation was by 16%. For CCOPE, at higher elevation sites, IMERG underestimated ice-initiated rain by 30% and underestimated warm rain by 70%. For ChOMPS, the underestimation at the Andes site was 33% for ice-initiated rain and 50% for warm rain. IMERG QPE for both field campaigns showed larger underestimations for warm rain periods and at higher elevations than for ice-initiated rain periods. Documenting how IMERG performance varies with terrain and microphysical regime may help guide improvements to satellite-based QPE.

The continental tropics play a leading role in the terrestrial energy, water, and carbon cycles. Land–atmosphere interactions are integral in the regulation of these fluxes across multiple spatial and temporal scales over tropical continents. We review here some of the important characteristics of tropical continental climates and how land–atmosphere interactions regulate them. Along with a wide range of climates, the tropics manifest a diverse array of land–atmosphere interactions. Broadly speaking, in tropical rainforest climates, light and energy are typically more limiting than precipitation and water supply for photosynthesis and evapotranspiration (ET), whereas in savanna and semi-arid climates, water is the critical regulator of surface fluxes and land–atmosphere interactions. We discuss the impact of the land surface, how it affects shallow and deep clouds, and how these clouds in turn can feed back to the surface by modulating surface radiation and precipitation. Some results from recent research suggest that shallow clouds may be especially critical to land–atmosphere interactions. On the other hand, the impact of land-surface conditions on deep convection appears to occur over larger, nonlocal scales and may be a more relevant land–atmosphere feedback mechanism in transitional dry-to-wet regions and climate regimes.

Increasing vapor pressure deficit (VPD) increases atmospheric demand for water. While increased evapotranspiration (ET) in response to increased atmospheric demand seems intuitive, plants are capable of reducing ET in response to increased VPD by closing their stomata. We examine which effect dominates the response to increasing VPD: atmospheric demand and increases in ET, or plant response (stomata closure) and decreases in ET. We use Penman-Monteith, combined with semi-empirical optimal stomatal regulation theory and underlying water use efficiency, to develop a theoretical framework for assessing ET response to VPD. The theory suggests that depending on the environment and plant characteristics, ET response to increasing VPD can vary from strongly decreasing to increasing, highlighting the diversity of plant water regulation strategies. The ET response varies due to: 1) climate, with tropical and temperate climates more likely to exhibit a positive ET response to increasing VPD than boreal and arctic climates; 2) photosynthesis strategy, with C3 plants more likely to exhibit a positive ET response than C4 plants; and 3) plant type, with crops more likely to exhibit a positive ET response, and shrubs and gymniosperm trees more likely to exhibit a negative ET response. These results, derived from previous literature connecting plant parameters to plant and climate characteristics, highlight the utility of our simplified framework for understanding complex land atmosphere systems in terms of idealized scenarios in which ET responds to VPD only. This response is otherwise challenging to assess in an environment where many processes co-evolve together.

The Chilean Coastal Orographic Precipitation Experiment (CCOPE) was conducted during the austral winter of 2015 (May-August) in the Nahuelbuta Mountains (peak elevation 1.3 km MSL) of southern Chile (388S). CCOPE used soundings, two profiling Micro Rain Radars, a Parsivel disdrometer, and a rain gauge network to characterize warm and ice-initiated rain regimes and explore their consequences for orographic precipitation. Thirty-three percent of foothill rainfall fell during warm rain periods, while 50% of rainfall fell during ice-initiated periods. Warmrain drop size distributions were characterized by many more and relatively smaller drops than ice-initiated drop size distributions. Both the portion and properties of warm and iceinitiated rainfall compare favorably with observations of coastal mountain rainfall at a similar latitude in California. Orographic enhancement is consistently strong for rain of both types, suggesting that seeding from ice aloft is not a requisite for large orographic enhancement. While the data suggest that orographic enhancement may be greater during warm rain regimes, the difference in orographic enhancement between regimes is not significant. Sounding launches indicate that differences in orographic enhancement are not easily explainable by differences in low-level moisture flux or nondimensional mountain height between the regimes.

Downwelling solar, Q(si), and longwave, Q(li), irradiances at the earth's surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Q(si) and Q(li) in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70-80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

Everyone taking field observations has a story of data collection gone wrong, and in most cases, the errors in the data are immediately obvious. A more challenging problem occurs when the errors are insidious, i.e., not readily detectable, and the error-laden data appear useful for model testing and development. We present two case studies, one related to the water balance in the snow-fed Tuolumne River, Sierra Nevada, California, combined with modeling using the Distributed Hydrology Soil Vegetation Model (DHSVM); and one related to the energy balance at Snoqualmie Pass, Washington, combined with modeling using the Structure for Unifying Multiple Modeling Alternatives (SUMMA). In the Tuolumne, modeled streamflow in 1 year was more than twice as large as observed; at Snoqualmie, modeled nighttime surface temperatures were biased by about 110 degrees C. Both appeared to be modeling failures, until detective work uncovered observational errors. We conclude with a discussion of what these cases teach us about science in an age of specialized research, when one person collects data, a separate person conducts model simulations, and a computer is charged with data quality assurance.

We introduce a quality controlled observational atmospheric, snow, and soil data set from Snoqualmie Pass, Washington, USA, to enable testing of hydrometeorological and snow process representations within a rain‐snow transitional climate where existing observations are sparse and limited. Continuous meteorological forcing (including air temperature, total precipitation, wind speed, specific humidity, air pressure, and short and longwave irradiance) are provided at hourly intervals for a 24 year historical period (water years 1989–2012) and at half‐hourly intervals for a more recent period (water years 2013–2015), separated based on the availability of observations. The majority of missing data were filled with biased‐corrected reanalysis model values (using NLDAS). Additional observations include 40 years of snow board new snow accumulation, multiple measurements of total snow depth, and manual snow pits, while more recent years include subdaily surface temperature, snowpack drainage, soil moisture and temperature profiles, and eddy covariance‐derived turbulent heat flux. This data set is ideal for testing hypotheses about energy balance, soil, and snow processes in the rain‐snow transition zone.

The performance of existing energy balance snow models exhibits a large spread in the simulated snow water equivalent, snow depth, albedo, and surface temperature. Indentifying poor model representations of physical processes within intercomparison studies is difficult due to multiple differences between models as well as non-orthogonal metrics used. Efforts to overcome these obstacles for model development have focused on a modeling framework that allows multiple representations of each physical process within one structure. However, there still exists a need for snow study sites within complex terrain that observe enough model states and fluxes to constrain model choices. In this study we focus on an intensive snow observational site located in the maritime-transitional snow climate of Snoqualmie Pass WA (Figure 1). The transitional zone has been previously identified as a difficult climate to simulate …