Phenology Inspiration

Figure 1:

Figure 1: The length of the growing season over the past century.

In today’s post, I thought I’d demonstrate how I am personally using this information.

The phenology vignette has inspired me to start me our data collection. Phenology affects so many different processes from carbon sequestration to pollination to food supply (and so much more!).

For my inspiration, I am using a salt marsh. Salt marsh ecology is affected by the seasonal cycle of the dominate vegetation species: from when a marsh first starts to ‘green up’ in the spring to when it reaches peak biomass in the late summer, to when it starts to senesce in the fall. While these phenological events will naturally vary year to year, the expansion of the growing season (Fig. 1) suggests that the long-term trend could mean an earlier greening and a later browning.

All long-term monitoring projects started with a question, a single data point, and a dedicated scientist. Why not start monitoring the phenology of a salt marsh near me?

Art and Science

Fig. 2: Art and Science are connected by wonder. Credit

Fig. 2: Art and Science are connected by wonder. Credit: openlabresearch

Coreen Weilminster showed a fantastic slide at the Patuxent River Conference. Science and Art are twins connected by Wonder (Fig. 2).

Starting next week, I have decided to take a picture of a salt marsh at the Delaware National Estuarine Research Reserve every Wednesday from the same location for as long as possible.

Weekly photographs could allow me to ‘see’ and archive the seasonal transition of that marsh over the course of a year. Through these images, I could estimate when the marsh first wakes up and when it first dies back in the fall.

If I am truly ambitious, I will continue this effort for a few years and start my own phenology data set. At the very least, I will have a great collage of the transition of a marsh from summer to winter!

A test photograph...could this be the location I pick?

A test photograph…could this be the location I pick?

 

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Vibrio Vignette: Part II

Welcome back! This week, we continue our Vibrio vignette initiated last week!

Top Solid line: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over the period from 2004 to 2014. Dashed Line: Modelled annual probability based on the winter extreme climate index, TXn. Bottom Solid Line: Annual integral of probability of Vibrio vulnificus occurrence at the CBNERRs Taskinas Creek site. Dashed Line: Modelled annual probability based on the spring extreme climate index, TX90p.

Top Solid line: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over the period from 2004 to 2014. Dashed Line: Modeled annual probability based on the winter extreme climate index, TXn. Bottom Solid Line: Annual integral of probability of Vibrio vulnificus occurrence at the CBNERRs Taskinas Creek site. Dashed Line: Modeled annual probability based on the spring extreme climate index, TX90p.

To understand how the intensity of the Vibrio probability combines with how long Vibrio persists, we summed up the probabilities over a year from 2004 to 2014.  There is substantial interannual variability in the integrated probabilities for both V. cholarae and V. vulnificus.

Since much of the interannual change for both species is associated with temperature, we analyze our thermal extreme climate indices to determine whether they might predict Vibrio.  A number of indices have modest predictive power. But, the best model for V. cholerae occurs with the winter season (December, January, February) integrated TXn index, which measures the coldest maximum daily temperature in a month. High values are associated with a lack of cold spells. This index is positively correlated with the winter North Atlantic Oscillation index, and also with growing season length and the Warm Spell Duration Index.

For the V. vulnificus, the best fit model is based on the spring (March, April, May) integrated TX90p index. This index is a measure of the number of days in a month when the maximum daily temperature exceeds the 90th percentile. Thus, warm spring temperatures are correlated with higher annual V. vulnificus. This index is also negatively correlated with TX10p and TN10p, measures of very cold events and slightly positively correlated with the Atlantic Multidecadal Oscillation.

Top Squares: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over the period from 1900 to 2100 based on observed TXn. Dots: Past and future annual integral of probability based on the CMIP 5 model ensemble of winter extreme climate index, TXn. Bottom Squares: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Taskinas Creek site over the period from 1900 to 2100 based on observed TX90p. Dots: Past and future annual integral of probability based on the CMIP 5 model ensemble of winter extreme climate index, TX90p.

Top Squares: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over the period from 1900 to 2100 based on observed TXn. Dots: Past and future annual integral of probability based on the CMIP 5 model ensemble of winter extreme climate index, TXn. Bottom Squares: Annual integral of probability of Vibrio cholarae occurrence at the CBNERRs Taskinas Creek site over the period from 1900 to 2100 based on observed TX90p. Dots: Past and future annual integral of probability based on the CMIP 5 model ensemble of winter extreme climate index, TX90p.

Because we have only eleven data points for IAP calculated from the SWMP dataset, we do not have sufficient information to begin to validate the model linking extreme event indices to the Vibrio presence model. This would require sufficient data to separate training data from the data used in development of the model.  However, under the assumption that the model has some skill, linking the extreme event indices to the Vibrio presence models allows us to make both backwads predictions and forecasts of Vibrio presence. Back predictions for V. cholarae and V. vulnificus use the observed extreme event indices with North and South Chesapeake regions aggregated together. Back predictions and forecast predictions of the climate model ensemble are also shown (see right). The model range and variability are consistent with the observed estimates for annual integrals of the probability of occurrence. More interannual variability is observed in both the data based model and the numerical model based V. cholera estimates than for the V. vulnificus estimates.

The water quality sonde and weather station at Jug Bay.

The water quality sonde and weather station at Jug Bay. Credit

Both time series exhibit an increasing trend as is expected based on the linear trend analysis for both the TXn and TX90p indices. There is evidence for increasing numbers of human health impacts from both Vibrio species (Newton et al. 2012), however the reporting standards have changed over time, and the links between Vibrio in the water, and infection from shellfish or direct contact with a wound are complex to unravel. Thus, these models should not be interpreted as a justification for the increased cases of Vibrio infection in humans in recent years. These models do suggest that V. cholarae and V. vulnificus will continue to show increased probability of occurrence in Chesapeake Bay due to climate changed induced increases in temperature.

Works Cited

Brown, C.W., R.R. Hood, W. Long, J. Jacobs, D.L. Ramers, C. Wazniak, J.D. Wiggert, R. Wood, and J. Xu. 2013. Ecological forecasting in Chesapeake Bay: Using a mechanistic–empirical modeling approach. Journal of Marine Systems 125: 113–125. doi:10.1016/j.jmarsys.2012.12.007.

Jacobs, John M, Matt Rhodes, Christopher W Brown, Raleigh R Hood, Andrew Leight, Wen Long, and Robert Wood. 2014. Modeling and Forecasting the Distribution of Vibrio vulnificus in Chesapeake Bay. Journal of Applied Microbiology. doi:10.1111/jam.12624.

Louis, V.R., Estelle Russek-Cohen, Nipa Choopun, Irma N G Rivera, Brian Gangle, Sunny C Jiang, Andrea Rubin, Jonathan a Patz, Anwar Huq, and Rita R Colwell. 2003. Predictability of Vibrio cholerae in Chesapeake Bay. Applied and environmental microbiology 69: 2773–2785. doi:10.1128/AEM.69.5.2773.

De Magny, Guillaume Constantin, Wen Long, Christopher W. Brown, Raleigh R. Hood, Anwar Huq, Raghu Murtugudde, and Rita R. Colwell. 2009. Predicting the distribution of Vibrio spp. in the Chesapeake Bay: A vibrio cholerae case study. EcoHealth 6: 378–389. doi:10.1007/s10393-009-0273-6.

Newton, Anna, Magdalena Kendall, Duc J. Vugia, Olga L. Henao, and Barbara E. Mahon. 2012. Increasing Rates of Vibriosis in the United States, 1996–2010: Review of Surveillance Data From 2 Systems. Clin Infect Dis. 54: S391–S395. doi:10.1093/cid/cis243.Increasing.

Scallan, Elaine, Robert M. Hoekstra, Frederick J. Angulo, Robert V. Tauxe, Marc Alain Widdowson, Sharon L. Roy, Jeffery L. Jones, and Patricia M. Griffin. 2011. Foodborne illness acquired in the United States-Major pathogens. Emerging Infectious Diseases 17: 7–15. doi:10.3201/eid1701.P11101.

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Vibrio Vignette: Part 1

When I think of cholera, I usually envision a third world country where untreated sewage flows into drinking water. But the microbes that cause cholera (and other infections) are natives here in Chesapeake Bay. Rita Colwell, past director of the National Science Foundation did pioneering work on this topic. Several types of Vibrio bacteria live in the Bay. One species found in low salinity regions, V. cholarae, can cause cholera when contaminated shellfish or untreated water are ingested. Around 80 people get sick from this bacteria in the United States every year (Scallan et al. 2011). V. vulnificus is a different species that grows in higher salinity regions. Only about 100 V. vulnificus infections occur each year in the United States, but this infection can be dangerous, with nearly 85% of patients requiring hospitalization and a fatality rate of over 30% (Scallan et al. 2011).

To forecast V. cholera (Louis et al. 2003; De Magny et al. 2009) and V. vulnificus (Jacobs et al. 2014) in Chesapeake Bay, scientists have developed models based on observing what are the typical temperature and salinity when Vibrio occurs. In both models, higher temperatures suggest more Vibrio. This means that increasing temperatures could both increase the likelihood that Vibrio is present, and also lengthen the time period over which the bacteria occur.

Top Model of the percent probability of Vibrio cholarae presence at different temperatures and salinities. Below, Model of the percent probability of Vibrio vulnificus presence.

Top: Model of the percent probability of Vibrio cholarae presence at different temperatures and salinities. Below: Model of the percent probability of Vibrio vulnificus presence.

Top Probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over time. Bottom Probability of Vibrio vulnificus occurrence at the CBNERRs Taskinas Creek site.

Top: Probability of Vibrio cholarae occurrence at the CBNERRs Jug Bay site over time. Bottom: Probability of Vibrio vulnificus occurrence at the CBNERRs Taskinas Creek site.

These models have been applied across the bay using hydrodynamic models to predict the temperature and salinity of the environment (Brown et al. 2012; Jacobs et al. 2014).  However, at the largely freshwater Jug Bay CBNERR site (Maryland) and the mesohaline Taskinas Creek CBNERR site (Virginia), the SWMP data allow us to predict the likelihood of Vibrio species using data. The predicted seasonal pattern of V. cholarae at Jug Bay is high in summer due to high water temperatures, with little interannual variability. The early winter and spring seasons have much higher interannual variability, though probabilities generally remain below 50%. Most of this is due to temperature fluctuations. In contrast, the predicted seasonal cycle in V. vulnificus at Taskinas Creek is more concentrated around peak summertime temperatures with much lower probability of occurrence in late fall through early spring. Spring interannual variability is due to temperature variations, however the variability in late summer and fall is due more to salinity variability.

Stay tuned for Part 2 next week!

Works Cited

Brown, C.W., R.R. Hood, W. Long, J. Jacobs, D.L. Ramers, C. Wazniak, J.D. Wiggert, R. Wood, and J. Xu. 2013. Ecological forecasting in Chesapeake Bay: Using a mechanistic–empirical modeling approach. Journal of Marine Systems 125: 113–125. doi:10.1016/j.jmarsys.2012.12.007.

Jacobs, John M, Matt Rhodes, Christopher W Brown, Raleigh R Hood, Andrew Leight, Wen Long, and Robert Wood. 2014. Modeling and Forecasting the Distribution of Vibrio vulnificus in Chesapeake Bay. Journal of Applied Microbiology. doi:10.1111/jam.12624.

Louis, V.R., Estelle Russek-Cohen, Nipa Choopun, Irma N G Rivera, Brian Gangle, Sunny C Jiang, Andrea Rubin, Jonathan a Patz, Anwar Huq, and Rita R Colwell. 2003. Predictability of Vibrio cholerae in Chesapeake Bay. Applied and environmental microbiology 69: 2773–2785. doi:10.1128/AEM.69.5.2773.

De Magny, Guillaume Constantin, Wen Long, Christopher W. Brown, Raleigh R. Hood, Anwar Huq, Raghu Murtugudde, and Rita R. Colwell. 2009. Predicting the distribution of Vibrio spp. in the Chesapeake Bay: A vibrio cholerae case study. EcoHealth 6: 378–389. doi:10.1007/s10393-009-0273-6.

Newton, Anna, Magdalena Kendall, Duc J. Vugia, Olga L. Henao, and Barbara E. Mahon. 2012. Increasing Rates of Vibriosis in the United States, 1996–2010: Review of Surveillance Data From 2 Systems. Clin Infect Dis. 54: S391–S395. doi:10.1093/cid/cis243.Increasing.

Scallan, Elaine, Robert M. Hoekstra, Frederick J. Angulo, Robert V. Tauxe, Marc Alain Widdowson, Sharon L. Roy, Jeffery L. Jones, and Patricia M. Griffin. 2011. Foodborne illness acquired in the United States-Major pathogens. Emerging Infectious Diseases 17: 7–15. doi:10.3201/eid1701.P11101.

 

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Patuxent River Conference 2016

IMG_20160623_123938763

A lovely view during lunch at SERC.

On Thursday, Jenn Raulin and Coreen Weilminster from the Chesapeake Bay National Estuarine Research Reserve in Maryland, along with me, gave an immersive breakout session at the Patuxent River Conference (#PaxCon2016).

PaxCon was in Edgewater, Maryland at the Smithsonian Environmental Research Center. This was my first time at SERC and I highly recommend checking out the trails!

The PaxCon2016 schedule!

The PaxCon2016 schedule!

The Patuxent River Conference was a change of pace from the large, national science conferences I typically attend; the total attendance was near 100 people, most of whom were informal educators. Presentations were 50 minutes long, which gave the presenters a chance to be more interactive with there audience.

Jenn, Coreen, and I ran an immersive breakout session in the 11 am time slot titled “Climate Stories of the Chesapeake: Using Data from the Patuxent River and Taskinas Creek (VA) to Assess Climate Variability and Extreme Change.”

I gave a short talk about our Phenology vignette, which encapsulates Jug Bay, one of the CBNERR-MD components that is situated on the Patuxent River. Coreen and Jenn also gave a short talk on potential educational uses of this information.

Archaeology finds in the Patuxent watershed!

Archaeology finds in the Patuxent watershed!

With about 20 minutes left in the session, Jenn and Coreen facilitated a challenge for the ~30 attendees at our talk. The participates were divided into three groups, given markers, flip charts, and a print out of the presentation and then assigned a scenario: 1) a table at an open house, 2) the middle school field trip, 3) and a visit with city/town officials.

Each group worked under the short time crunch, in a group of new acquaintances,  to come up with a plan on how to incorporate the information from the Phenology vignette into their scenario. Each group then had to present it to the larger group. It was really exciting to see the different ideas emerge with just a handful of minutes!

The session went by quickly and I am still processing the information I learned! Throughout the day, I saw excellent sessions on raptors, archaeology, citizen science campaigns, and how to communicate controversial subjects!

It was a lot of fun and I send a huge thank you to Jenn and Coreen for inviting me to present with them!

A lovely view during lunch at SERC.

Jug Bay is one of three CBNERR-MD components.

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Work in progress: Collaborative Template

Honey bees are great a communicating! Credit: Pixabay

Honey bees are great at communicating! Credit: Pixabay

One of the products I have been working on for this project deals not just with the cool findings, but rather how we (researchers from UMCES) collaborated with educators, managers, and researchers from the Chesapeake Bay National Estuarine Research Reserve (CBNERR) of Maryland and Virginia, NOAA, and Chesapeake Environmental Communications to decide what we should be investigating.

From the beginning, our goal was to have this be a collaborative venture, not a clientele. This may not sound “revolutionary” but it was a first for me!

This is sort of a Part I post. When I starting write about our collaborative approach, I noticed my word count was getting longer and longer. So I stopped myself and thought 1) this is too long for a single blog post and 2) wow is our project approach very involved, dynamic, and complex!

Taking the Road Less Traveled

Figure 1: Traditional Projects have a linear flow while ours allowed for some wiggle room.

Figure 1: Traditional Projects have a linear flow while ours allowed for some wiggle room.

In most “traditional” research projects, scientists will write a research proposal laying out a clearly defined testable hypothesis and the methodology they plan to do in order to accept or reject that hypothesis (Figure 1). It is a fairly straightforward path that gets you straight from the proposed project idea to a result.

This proposal was more unique than a “traditional” project since the path from the hypothesis (“how is climate change affecting the ecosystems in Chesapeake Bay?”) end goal was not direct (Figure 1). This meant that the methodology could be defined as the project was ongoing, with input from other partners. The ultimate goal of this approach was to have our end-users be part of that process from day 1 so that the products created would truly be “end-user ready”.

In the “traditional” model, we would have “went away” for 2 years to do our rigorous science and had very little collaborative input. In this traditional scenario, that end product may not be useful and could just end up on shelf collecting dust. Our scenario still follows the scientific method, but allowed more wiggle room for input.

A Scary New World

flowers-desk-office-vintage

Communicating can be fun!

Collaboration can be tough and even time consuming from an administrative standpoint, but the end result is more fruitful. After all, it’s better to have 2, 3, 4+ sets of eyes than just 1!

For this project to be successful, we had to think not just about how we would go about testing the research question, but how we would communicate our methodology, results, and product design to the CBNERR staffs. We also had to think of ways to allow everyone to transparently see our work and have real-time input. (That is what a collaboration is after all!)

To do this, we implemented multiple tools to aid in the communication and collaboration aspects of this project.

Be like the talking flowers in Alice in Wonderland! Credit

Be like the talking flowers in Alice in Wonderland! Credit

Think Tanks: By the end of the project, we will have had 4 Think Tanks. These are in person meetings where we share ideas, findings, and talk about next steps forward.

Workplan: After the first Think Tank, the group decided we needed a Workplan for a “plan of attack” of this project. Over a few weeks, we wrote a DPSIR (Driver, Pressure, State, Impact, Response) framework that had multiple back and forths with our partners.

Blog: To increase the communication of what we were doing in real time, I started this weekly blog. It gives insight into our thought processes and allows for real time inputs (comments or emails welcome).

Rankable Matrix: When it came time to selected which environmental problems we would be investigating, I sent around a rankable matrix of topics that we realistically could do with the time and resources available to us. The highest ranked were our “vignettes.”

Monthly Emails: To increase the communication on milestones, hurdles, and what we were doing, monthly email updates were initiated last October; this was a lessoned learned that we needed more than just the blog.

As you can (hopefully) see, we put a lot of time and effort into this project not just analytically, but also communicably. Stay tuned for more specifics on this work-in-progress template!

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Same Figure, Different Audiences

This week’s post will be a newer layout as it with give you an inside to a project meeting. That is where many great ideas are born!

IMG_20160517_145855212Back on May 17th, Jenn Raulin (the Reserve Manager of the Maryland Chesapeake Bay National Estuarine Research Reserve) and Coreen Weilminster (the Education coordinator of the Maryland Chesapeake Bay National Estuarine Research Reserve) met with me (Hi, I’m Kari!) to plan our immersive breakout session for the 2016 Patuxent River Conference.

The primary audience of this year’s PaxCon2016 are informal educators (which happens to be one of the end-users for this project!). This is a new world for me and I am excited to get the opportunity to meet, hear, and speak to educators throughout the Patuxent River watershed.

Jenn, Coreen, and I will be giving an immersive breakout session (another first for me!). The general  presentation layout will be: 1) an introduction to this project, 2) a scientific presentation of data, 3) an overview of how the Maryland CBNERR educators could use this data, and 4) a breakout session tasking attendees to translate the data based on different scenarios.

You can bet that the next few week’s posts will discuss the details above in more depth.

This week’s epiphany!

I am currently preparing my part of the presentation for the 2016 Patuxent River Conference.

During our May 17th meeting, Jenn and Coreen came up with a great idea. Since this is a Science Translation session, it makes sense to show how we have translated data for different audiences.

That is how Figure 1 was born! This image shows the same information (growing season length) plotted 3 different ways based on the targeted audience.

Gigure 1:

Figure 1: The same information can be ‘packaged’ different ways based on the audience.

The first plot is for a scientific manuscript meant for a technical audience.

The second plot is for a white paper summary meant for the CBNERR staffs of Maryland and Virginia.

The third plot is Dave Jasinski’s creation for a general audience and grade school e-book on climate change.

I thought this image truly shows how science can be translated for different user groups and still be informative and substantive.

Stay tuned for more on a technical scientist’s journey (me) to be a better communicator!

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Status Update: Manuscripts submitted

Credit: Pixabay

Credit: Pixabay

This week’s post will be a status update since we hit a major milestone in the project!

As of Wednesday, the companion manuscripts written for this project have been submitted for peer review in the journal Estuaries and Coasts.

As explained in a previous post, the peer review process will allow other experts to examine our work and provide construct input for improvements. Acceptance is not guaranteed, but submitting gets us one step closer to be able to publish this work in a major scientific journal.

The Manuscripts!

For this project, two companion manuscripts were written and submitted together.

CHESAPEAKE BAY -- A Maryland Blue Crab tries to escape from a basket aboard the Tempest June 6, 2012. Bob Evans is captain of the Tempest and was awarded the Highliner award in 2010 in recognition of his contributions to the fishing community. (U.S. Air Force photo/Staff Sgt. Benjamin Wilson)

Credit: Flickr and U.S. Air Force photo/Staff Sgt. Benjamin Wilson

Manuscript 1 is a synthesis of the climate extreme patterns of change and variability in the near-shore Chesapeake Bay region. This paper looks at historical changes as well as future projections and includes statistical confidence in those trends. It also discusses correlations with major rivers and teleconnection indices.

Manuscript 2 demonstrates the application of those climate indices by investigating 4 environmental problems currently facing Chesapeake Bay, as represented by the Chesapeake Bay National Estuarine Research Reserves. The 4 “vignettes” include, SAV diebacks with the frequency of warm summer days, total nitrogen loading with precipitation frequency, phenology changes with an expansion of the growing season, Vibrio occurrence with more warm days.

Look out for future project updates to learn how we do in the peer review process, as well as other products in development!

 

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Everyone has a “Story”

When I present this work, more often than not, I find someone who has a personal story they can share to put these climate changes in real life perspective.

Today, I thought I would share one that has come up twice: ticks.

The Growing Season has changed

Winter is getting shorter in Chesapeake Bay!

Winter is getting shorter in Chesapeake Bay!

This has come up a few times: the growing season has gotten longer in Chesapeake Bay. This also means that our winter has gotten shorter (by an average of 30 days!).

For this study, we have taken our calculated climate extreme indices and applied them to better understand ecosystem and environmental changes. For example, as Victoria Coles explains in a previous post, we related the increase in warm summer days to submerged aquatic vegetation die-back events.

But these indices we calculated are not limited to just a handful of “ecological vignettes.” In fact, our hope is that others could use these indices to understand problems they are concerned about!

Ticks!

index

A dog tick. Credit: Wikimedia Commons

If you live in the near-shore Chesapeake Bay region, you probably know about ticks. If you work in a marsh, you probably have the routine of “tick checks” down to an art.

When I present this work on the growing season length changes, on more than one occasion, the subject of ticks have come up. Specifically, audience members who have worked outdoors for years to decades, have noticed the “tick season” getting longer. Some have starting seeing ticks year round.

Now this is not a quantitative analysis, but anecdotal. However, I thought it was a great example of how this work can be used to help educate about the climate changes people in the near-shore Chesapeake Bay area have already experienced in their life time.

Personal stories, like a longer tick season, are exciting to hear about (well, the story, not the ticks). So keep them coming!

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The Peer Review Process

We have been working hard writing, editing, and formatting portions of the work you have been reading about over the last few months. Why? Because we will be submitting two manuscripts soon for consideration to be published in a scientific journal.

This is a great time to briefly talk about the peer review process and why it is so important.

Why is Peer Review so Important?

Figure 1:

Figure 1: The peer review process. Credit

To many, myself included, reading a beautifully worded newspaper summary or looking at a conceptual diagram is more appealing that reading through a technical 16 page scientific paper.

But how do you know that the information summarized on your Facebook newsfeed is a scientific finding and not just an untrue ramble? The answer usually lies in that citation tucked away at the bottom of an image or quoted in a blog post.

That citation usually means it went through a peer review process.

Okay, what is Peer Review?

Peer review is a review of scientific material by the peers in that field (just like it sounds!). It is a rigorous, and fairly standardized, process defined by the journal you hope to publish in.

Here are the general steps (as depicted in Figure 1). First, the researcher(s) conduct their experiment or computations using the scientific method of formulating a hypothesis then proceeding to test that hypothesis using statistics, etc.. Next, those results, methodologies, and implications are written concisely, providing enough information that a fellow researcher could try that analysis themselves!

PeerReview

To some, the peer review process can feel like going to battle, but it is necessary to critically assess the validity of new information.

From here, that manuscript will be submitted to the journal of choice for peer review. Before (and if) that hard work can be accepted by the scientific community (and everyone else!), an average of 2-3 experts in that subject (but not involved whatsoever with the work) will read and review the manuscript.

A good peer reviewer will critique and evaluate everything from the method used to the interpretation of the results to even the style of writing.

Acceptance is not guaranteed! Most journals give a few choices to a peer review: accept as is, accept with major revisions, accept with minor revisions, reject. If revisions are required, which is fairly common, that researcher is given 1-2 months to make the corrections and/or suggestions.

The peer review process is not a proof-reading, rather meant to critically review if a result is scientifically valid. If a paper is accepted, you can be assured that an expert read and scrutinized that work 1-2 times before giving it a thumbs up.

This whole process can take weeks to months to even years depending on the degree of revisions needed! But the bottom line is that a scientific work that goes through this rigorous peer review is a defensible, new scientific finding.

So the next time you read a cool theory, or seem a neat infographic, search for that citation. If you do not see one, you’ll have to ask yourself: is this a defensible result or just a wild theory?

 

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What is a Climate Normal?

In this investigation, you’ve heard me talk a lot about past trends and future trends. Both are important perspectives in understanding how climate has changed (past), how it will possibility change (future), and the confidence we have in those future projections (by comparing the observed past to the predicted future).

But what about the present?

Establishing a baseline of present climate extreme conditions can be just as important! This allows us to understand how this are changing from what is “average”.

What is normal anyway?

Let’s use a weather example. Have you ever heard a meteorologist say something like “today’s high is 10°F above the average for today” to emphasize a really hot day? In order for this statement to exist, that meteorologist has to know what that average temperature is.

Figure 1: The month climate normals for Baltimore Maryalnd are the mean temperature and precipitation from 1981 to 2010.

Figure 1: The monthly climate normals for Baltimore, Maryland are the mean temperature and precipitation from 1981 to 2010. Credit: US Climate Data

Averages in weather often use a climate normal, which are 30 year means. By averaging 3 decades worth of data, scientists can have a pretty good idea of the “mean” condition. But mean does not translate to expected! For example, a mean of 50°F could be the result of a 25°F day and a 75°F day.

Figure 1 shows the monthly average precipitation and high and low temperatures for Baltimore, MD using the 30 year monthly mean of weather data from 1981 to 2010. The high temperature in Baltimore yesterday (May 15) was ~60°F, so we could say it was ~15°F cooler than normal.

Climate normals are updated every 10 years, which allows natural variability (such as El Nino effects) as well as climate changes to be included in these means.

Climate Normals from our study!

Now that you know that calculating climate normals is gets us a baseline, what does the climate extremes in the near-shore Chesapeake Bay region look like? Check out Table 1 to find out!

Table 1:

Table 1: The climate normals for all annual-based climate extreme indices in this study. Shaded cells mean there was a significant difference from the current (1981 to 2010) climate normal from the old (1951 to 1980).

Data like this can be used to assess the future climate extreme trends as they unfold as well as tell us if a year was wetter or drier than “normal.”

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