The Central American Dry Corridor experienced five consecutive years of drought from 2015 to 2019. Here, we find that the severity of this drought was driven primarily by rainfall deficits in July-August. To determine if the magnitude of this event was outside the range of natural variability, we apply a statistical resampling method to observations that emulates internal climate variability. Our analyses show that droughts similar to the 2015-2019 event are possible, although extremely rare, even without anthropogenic influences. Persistent droughts in our ensemble are consistently linked to positive anomalies of the Caribbean Low-Level Jet. We also examine the effects of temperature on soil moisture during this drought using the Palmer Drought Severity Index and show that anthropogenic warming increases the likelihood of severe deficits. Multi-year droughts are likely to worsen by the end of the 21st century due to the compound effects of anthropogenic climate change.

We use a statistical emulation technique to construct synthetic ensembles of global and regional sea-air carbon dioxide (CO2) flux from four observation-based products over 1985-2014. Much like ensembles of Earth system models that are constructed by perturbing their initial conditions, our synthetic ensemble members exhibit different phasing of internal variability and a common externally forced signal. Our synthetic ensembles illustrate an important role for internal variability in the temporal evolution of global and regional CO2 flux and produce a wide range of possible trends over 1990-1999 and 2000-2009. We assume a specific externally forced signal and calculate the likelihood of the observed trend given the distribution of synthetic trends during these two periods. Over the decade 1990-1999, three of the four observation-based products exhibit small negative trends in globally integrated sea-air CO2 flux (i.e., enhanced ocean CO2 absorption with time) that are highly probable (44-72% chance of occurrence) in their respective synthetic trend distributions. Over the decade 2000-2009, however, three of the four products show large negative trends in globally integrated sea-air CO2 flux that are somewhat improbable (17-19% chance of occurrence). Our synthetic ensembles suggest that the largest observation-based positive trends in global and Southern Ocean CO2 flux over 1990-1999 and the largest negative trends over 2000-2009 are somewhat improbable (<30% chance of occurrence). Our approach provides a new understanding of the role of internal and external processes in driving sea-air CO2 flux variability.

Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long-term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long-term trends in the presence of internal variability. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.

Ocean primary production constitutes approximately half of global biospheric production, affecting both fisheries productivity and biogeochemical cycling. Although climate change is predicted to affect the ocean's biological productivity, the extent of the global impact is poorly quantified. Assessing changes in the ocean biosphere using remote sensing data is challenged by the relatively short length of the observational record, restricting our ability to disentangle fluctuations in internal variability from forced anthropogenic trends. Additionally, the majority of ocean circulation models with embedded biogeochemistry do not skillfully predict observational records of ocean chlorophyll at ocean time series locations. To overcome these limitations, we have constructed a synthetic ensemble of global ocean chlorophyll concentration. By employing statistical resampling methods to the SeaWiFS and MODIS ocean color datasets and creating surrogate climate modes of ENSO and PDO, we quantify the range of internal climate variability in the 20 year observational record and create multiple alternate realities for the possible evolution of the ocean biosphere over time. Our synthetic ensemble can be used for a variety of purposes, including diagnosing patterns of internal variability and emergence of anthropogenic trends in observed chlorophyll, and validating Earth system model representation of such variability.

The 2021 Pacific Northwest heatwave featured record-smashing high temperatures, raising questions about whether extremes are changing faster than the mean, and challenging our ability to estimate the probability of the event. Here, we identify and draw on the strong relationship between the climatological higher-order statistics of temperature (skewness and kurtosis) and the magnitude of extreme events to quantify the likelihood of comparable events using a large climate model ensemble (CESM2-LE). In general, CESM2 can simulate temperature anomalies as extreme as those observed in 2021, but they are rare: temperature anomalies that exceed 4.5σ occur with an approximate frequency of one in a hundred thousand years. The historical data does not indicate that the upper tail of temperature is warming faster than the mean; however, future projections for locations with similar climatological moments to the Pacific Northwest do show significant positive trends in the probability of the most extreme events.