Grassland fire ecology has roots in the late Miocene

That fire facilitated the late Miocene C4grassland expansion is widely suspected but poorly documented. Fire potentially tied global climate to this profound biosphere transition by serving as a regional-to-local driver of vegetation change. In modern environments, seasonal extremes in moisture amplify the occurrence of fire, disturbing forest ecosystems to create niche space for flammable grasses, which in turn provide fuel for frequent fires. On the Indian subcontinent, C4expansion was accompanied by increased seasonal extremes in rainfall (evidenced by δ18Ocarbonate), which set the stage for fuel accumulation and fire-linked clearance during wet-to-dry seasonal transitions. Here, we test the role of fire directly by examining the abundance and distribution patterns of fire-derived polycyclic aromatic hydrocarbons (PAHs) and terrestrial vegetation signatures inn-alkane carbon isotopes from paleosol samples of the Siwalik Group (Pakistan). Two million years before the C4grassland transition, fire-derived PAH concentrations increased as conifer vegetation declined, as indicated by a decrease in retene. This early increase in molecular fire signatures suggests a transition to more fire-prone vegetation such as a C3grassland and/or dry deciduous woodland. Between 8.0 and 6.0 million years ago, fire, precipitation seasonality, and C4-grass dominance increased simultaneously (within resolution) as marked by sharp increases in fire-derived PAHs, δ18Ocarbonate, and13C enrichment inn-alkanes diagnostic of C4grasses. The strong association of evidence for fire occurrence, vegetation change, and landscape opening indicates that a dynamic fire–grassland feedback system was both a necessary precondition and a driver for grassland ecology during the first emergence of C4grasslands.

The bivalve <i>Glycymeris planicostalis</i> as a high-resolution paleoclimate archive for the Rupelian (Early Oligocene) of central Europe

Current global warming is likely to result in a unipolar glaciated world with unpredictable repercussions on atmospheric and oceanic circulation patterns. These changes are expected to affect seasonal extremes and the year-to-year variability of seasonality. To better constrain the mode and tempo of the anticipated changes, climatologists require ultra-high-resolution proxy data of time intervals in the past, e.g., the Oligocene, during which boundary conditions were similar to those predicted for the near future. In the present paper, we assess whether such information can be obtained from shells of the long-lived bivalve mollusk Glycymeris planicostalis from the late Rupelian of the Mainz Basin, Germany. Our results indicate that the studied shells are pristinely preserved and provide an excellent archive for reconstructing changes of sea surface temperature on seasonal to interannual timescales. Shells of G. planicostalis grew uninterruptedly during winter and summer and therefore recorded the full seasonal temperature amplitude that prevailed in the Mainz Basin ~ 30 Ma. Absolute sea surface temperature data were reconstructed from δ18Oshell values assuming a δ18Owater signature that was extrapolated from coeval sirenian tooth enamel. Reconstructed values range between 12.3 and 22.0 °C and agree well with previous estimates based on planktonic foraminifera and shark teeth. However, temperatures during seasonal extremes vary greatly on interannual timescales. Mathematically re-sampled (i.e., corrected for uneven number of samples per annual increment) winter and summer temperatures averaged over 40 annual increments of three specimens equal 13.6 ± 0.8 and 17.3 ± 1.2 °C, respectively. Such high-resolution paleoclimate information can be highly relevant for numerical climate studies aiming to predict possible future climates in a unipolar glaciated or, ultimately, polar-ice-free world.

Characterizing the Potential Predictability of Seasonal, Station-Based Heavy Precipitation Accumulations and Extreme Dry Spell Durations*

The release of seasonal (and longer) predictions of various climatological quantities is now routine. While undoubtedly devastating to lives and livelihoods, it is unclear whether seasonal extremes in precipitation—for example, extreme dry spells leading to droughts or heavy precipitation events leading to flooding—represent a feasible target for these predictions, that is, whether they are potentially predictable or are instead inherently unpredictable more than a few days to weeks in advance. This paper assesses the potential for predicting seasonal extremes in observed precipitation as a function of region and time of year by decomposing the station-based variance into that attributable to short-memory behavior of typical meteorological events—as generated from station-specific, seasonally varying, daily time-scale stationary stochastic weather models (SSWMs)—and that attributable to longer-time-scale, potentially predictable changes in precipitation-producing processes. Findings suggest the potential for making skillful predictions of seasonal precipitation extremes over the United States is enhanced (reduced) during the cool (warm) season, particularly for heavy precipitation event accumulations. Further, this potential is accentuated along the West Coast, around the Great Lakes, and over the central plains and Ohio River valley but is diminished over the Northeast and northern Great Plains. However, findings also suggest the potential for producing seasonal (and longer) predictions of seasonal precipitation extremes is spatially and seasonally dependent. As such, this paper includes supplemental material for the potentially predictable variance of seasonal extreme dry spell lengths, heavy event accumulations, and total accumulations at 774 stations across all 365 days so readers can evaluate the potential predictability for the location, timing, and metric of most relevance to them.